Sur la question de sous-représentation des classes populaires en politique : https://democratiserlapolitique.org/

En réalité, d'un pdv règlementaire il était obligé d'avoir les clefs en main (impossible de déléguer son pouvoir)

Quote from Nell Sonnemann https://www.findagrave.com/memorial/80146324/nell_battle-sonnemann no Wikipedia page it seems.

[[Art on My Mind by bell hooks]] visual politics, 1995.

hay á

Question : Pourquoi change-t-on les attributs dans le fichier .js alors qu'on peut directement le faire dans le fichier .html ? Par exemple, dans la vidéo, pourquoi se casser la tête avec du Java, alors qu'on a juste à modifier la valeur de "src" directement dans le fichier HTML ? Pareil pour le "alt" d'aillerus.

eLife Assessment

This study presents a valuable approach for revealing large-scale brain attractor dynamics during resting states, task processing, and disease conditions using insights from Hopfield neural networks. The evidence supporting the findings is convincing across the many datasets analysed. The work will be of broad interest to neuroscientists using neuroimaging data with interest in computational modelling of brain activity.

Reviewer #1 (Public review):

Summary:

Englert et al. proposed a functional connectivity-based Attractor Neural Network (fcANN) to reveal attractor states and activity flows across various conditions, including resting state, task-evoked, and pathological conditions. The large-scale brain attractors reconstructed by fcANNs are orthogonal organization, which is in line with the free-energy theoretical framework. Additionally, the fcANN demonstrates differences in attractor states between individuals with autism and typically developing individuals.

The study used seven datasets, which ensures robust replication and validation of generalization across various conditions. The study is a representative example that combines experimental evidence based on fcANN and the theoretical framework. The fcANN projection offers an interesting way of visualization, allowing researchers to observe attractor states and activity flow patterns directly. Overall, the study may offer valuable insights into brain dynamics and computational neuroscience.

Comments on revision:

The authors have addressed my previous concerns and substantially improved the manuscript. Fig.4 and Fig.5 still keep fcHNN rather than the updated fcANN.

Reviewer #2 (Public review):

Summary:

Englert et al. use a novel modelling approach called functional connectome-based Hopfield Neural Networks (fcHNN) to describe spontaneous and task-evoked brain activity, and the alterations in brain disorders. Given its novelty, the authors first validate the model parameters (the temperature and noise) with empirical resting-state function data and against null models. Through the optimisation of the temperature parameter, they first show that the optimal number of attractor states is four before fixing the optimal noise that best reflects the empirical data, through stochastic relaxation. Then, they demonstrate how these fcHNN generated dynamics predict task-based functional activity relating to pain and self-regulation. To do so, they characterise the different brain states (here as different conditions of the experimental pain paradigm) in terms of the distribution of the data on the fcHNN projections and flow-analysis. Lastly, a similar analysis was performed on a population with autism condition. Through Hopfield modeling, this work proposes a comprehensive framework that links various types of functional activity under a unified interpretation with high predictive validity.

Strengths:

The phenomenological nature of the Hopfield model and its validation across multiple datasets presents a comprehensive and intuitive framework for the analysis of functional activity. The results presented in this work further motivate the study of phenomenological models as an adequate mechanistic characterisation of large-scale brain activity.

Following up from Cole et al. 2016, the authors put forward a hypothesis that many of the changes to the brain activity, here, in terms of task-evoked and clinical data, can be inferred from the resting-state brain data alone. This brings together neatly the idea of different facets of brain activity emerging from a common space of functional (ghost) attractors.

The use of the null models motivates the benefit for non-linear dynamics in the context of phenomenological models when assessing the similarity to the real empirical data.

Comments on revision:

I am happy with how the authors addressed the comments and am happy to move ahead without further comments.

Author response:

The following is the authors’ response to the original reviews.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Englert et al. proposed a functional connectome-based Hopfield artificial neural network (fcHNN) architecture to reveal attractor states and activity flows across various conditions, including resting state, task-evoked, and pathological conditions. The fcHNN can reconstruct characteristics of resting-state and task-evoked brain activities. Additionally, the fcHNN demonstrates differences in attractor states between individuals with autism and typically developing individuals.

Strengths:

(1) The study used seven datasets, which somewhat ensures robust replication and validation of generalization across various conditions.

(2) The proposed fcHNN improves upon existing activity flow models by mimicking artificial neural networks, thereby enhancing the representational ability of the model. This advancement enables the model to more accurately reconstruct the dynamic characteristics of brain activity.

(3) The fcHNN projection offers an interesting visualization, allowing researchers to observe attractor states and activity flow patterns directly.

We are grateful to the reviewer for highlighting the robustness of our findings across multiple datasets and for appreciating the novelty and representational advantages of our fcHNN model (which has been renamed to fcANN in the revised manuscript).

Weaknesses:

(1) The fcHNN projection can offer low-dimensional dynamic visualizations, but its interpretability is limited, making it difficult to make strong claims based on these projections. The interpretability should be enhanced in the results and discussion.

We thank the reviewer for these important points. We agree that the interpretability of the low-dimensional projection is limited. In the revised manuscript, we have reframed the fcANN projection primarily as a visualization tool (see e.g. line 359) and moved the corresponding part of Figure 2 to the Supplementary Material (Supplementary Figure 2). We have also implemented a substantial revision of the manuscript, which now directly links our analysis to the novel theoretical framework of self-orthogonalizing attractor networks (Spisak & Friston, 2025), opening several new avenues in terms of interpretation and shedding light on the computational principles underlying attractor dynamics in the brain (see the revised introduction and the new section “Theoretical background”, starting at lines 128, but also the Mathematical Appendices 1-2 in the Supplementary Material for a comprehensive formal derivation). As part of these efforts, we now provide evidence for the brain’s functional organization approximating a special, computationally efficient class of attractor networks, the so-called Kanter-Sompolinsky projector network (Figure 2A-C, line 346, see also our answer to your next comment). This is exactly, what the theoretical framework of free-energy-minimizing attractor networks predicts.

(2) The presentation of results is not clear enough, including figures, wording, and statistical analysis, which contributes to the overall difficulty in understanding the manuscript. This lack of clarity in presenting key findings can obscure the insights that the study aims to convey, making it challenging for readers to fully grasp the implications and significance of the research.

We have thoroughly revised the manuscript for clarity in wording, figures (see e.g. lines 257, 482, 529 in the Results and lines 1128, 1266, 1300, 1367 in the Methods). We carefully improved statistical reporting and ensured that we always report test statistics, effect sizes and clearly refer to the null modelling approach used (e.g. lines 461, 542, 550, 565, 573, 619, as well as Figures 2-4). As absolute effect sizes, in many analyses, do not have a straightforward interpretation, we provided Glass’ , as a standardized effect size measure, expressing the distance of the true observation from the null distribution as a ratio of the null standard deviation. To further improve clarity, we now clearly define our research questions and the corresponding analyses and null models in the revised manuscript, both in the main text and in two new tables (Tables 1 and 2). We denoted research questions and null model with Q1-7 and NM1-5, respectively and refer to them at multiple instances when detailing the analyses and the results.

Reviewer #2 (Public Review):

Summary:

Englert et al. use a novel modelling approach called functional connectome-based Hopfield Neural Networks (fcHNN) to describe spontaneous and task-evoked brain activity and the alterations in brain disorders. Given its novelty, the authors first validate the model parameters (the temperature and noise) with empirical resting-state function data and against null models. Through the optimisation of the temperature parameter, they first show that the optimal number of attractor states is four before fixing the optimal noise that best reflects the empirical data, through stochastic relaxation. Then, they demonstrate how these fcHNN-generated dynamics predict task-based functional activity relating to pain and self-regulation. To do so, they characterise the different brain states (here as different conditions of the experimental pain paradigm) in terms of the distribution of the data on the fcHNN projections and flow analysis. Lastly, a similar analysis was performed on a population with autism condition. Through Hopfield modeling, this work proposes a comprehensive framework that links various types of functional activity under a unified interpretation with high predictive validity.

Strengths:

The phenomenological nature of the Hopfield model and its validation across multiple datasets presents a comprehensive and intuitive framework for the analysis of functional activity. The results presented in this work further motivate the study of phenomenological models as an adequate mechanistic characterisation of large-scale brain activity.

Following up on Cole et al. 2016, the authors put forward a hypothesis that many of the changes to the brain activity, here, in terms of task-evoked and clinical data, can be inferred from the resting-state brain data alone. This brings together neatly the idea of different facets of brain activity emerging from a common space of functional (ghost) attractors.

The use of the null models motivates the benefit of non-linear dynamics in the context of phenomenological models when assessing the similarity to the real empirical data.

We thank the reviewer for recognizing the comprehensive and intuitive nature of our framework and for acknowledging the strength of our hypothesis that diverse brain activity facets emerge from a common resting state attractor landscape.

Weaknesses:

While the use of the Hopfield model is neat and very well presented, it still begs the question of why to use the functional connectome (as derived by activity flow analysis from Cole et al. 2016). Deriving the functional connectome on the resting-state data that are then used for the analysis reads as circular.

We agree that starting from functional couplings to study dynamics is in stark contrast with the common practice of estimating the interregional couplings based on structural connectome data. We now explicitly discuss how this affects the scope of the questions we can address with the approach, with explicit notes on the inability of this approach to study the structure-function coupling and its limitations in deriving mechanistic insights at the level of biophysical implementation.

Line 894:

“The proposed approach is not without limitations. First, as the proposed approach does not incorporate information about anatomical connectivity and does not explitly model biophysical details. Thus, in its present form, the model is not suitable to study the structure-function coupling and cannot yiled mechanistic explanations underlying (altered) polysynaptic connections, at the level of biophysical details.”

We are confident, however, that our approach is not circular. At the high level, our approach can be considered as a function-to-function generative model, with twofold aims.

First, we link large-scale brain dynamics to theoretical artificial neural network models and show that the functional connectome display characteristics that render it as an exceptionally “well-behaving” attractor network (e.g. superior convergence properties, as contrasted against appropriate respective null models). In the revised manuscript, we have significantly improved upon this aspect by explicitly linking the fcANN model to the theoretical framework of self-orthogonalizing attractor networks (Spisak & Friston, 2025) (see the revised introduction and the new section “Theoretical background”, starting at lines 128, but also the Mathematical Appendices 1-2 in the Supplementary Material for a comprehensive formal derivation). As part of these efforts, we now provide evidence for the brain’s functional organization approximating a special, computationally efficient class of attractor networks, the so-called Kanter-Sompolinsky projector network (Figure 2A-C, line 346, see also our answer to your next comment). This is exactly, what the theoretical framework of free-energy-minimizing attractor networks predicts. This result is not circular, as the empirical model does not use the key mechanism (the Hebbian/anti-Hebbian learning rule) that induces self-orthogonalization in the theoretical framework. We clarify this in the revised manuscript, e.g. in line 736.

Second, we benchmark ability of the proposed function-to-function generative model to predict unseen data (new datasets) or data characteristics that are not directly encompassed in the connectivity matrix (e.g. non-Gaussian conditional dependencies, temporal autocorrelation, dynamical responses to perturbations on the system). These benchmarks are constructed against well defined null models, which provide reasonable references. We have now significantly improved the discussion of these null models in the revised manuscript (Tables 1 and 2, lines 257). We not only show, that our model - when reconstructing resting state dynamics - can generalize to unseen data over and beyond what is possible with the baseline descriptive measure (e.g. covariance measures and PCA), but also demonstrate the ability of the framework to reconstruct the effects of perturbations on this dynamics (such as task-evoked changes), based solely on the resting state data form another sample.

If the fcHNN derives the basins of four attractors that reflect the first two principal components of functional connectivity, it perhaps suffices to use the empirically derived components alone and project the task and clinical data on it without the need for the fcHNN framework.

We are thankful for the reviewer for highlighting this important point, which encouraged us to develop a detailed understanding of the origins of the close alignment between attractors and principal components (eigenvectors of the coupling matrix) and the corresponding (approximate) orthogonality. Here, we would like to emphasize that the attractor-eigenvector correspondence is by no means a general feature of any arbitrary attractor network. In fact, such networks are a very special class of attractor neural networks (the so-called Kanter-Sompolinsky projector neural network (Kanter & Sompolinsky, 1987)), with a high degree of computational efficiency, maximal memory capacity and perfect memory recall. It has been rigorously shown that in such networks, the eigenvectors of the coupling matrix (i.e. PCA on the timeseries data) and the attractors become equivalent (Kanter & Sompolinsky, 1987). This in turn made us ask the question, what are the learning and plasticity rules that drive attractor networks towards developing approximately orthogonal attractors? We found that this is a general tendency of networks obeying the free energy principle ( Figure 2A-C, line 346, see also our answer to your next comment). The formal derivation of this framework in now presented in an accompanying theoretical piece (Spisak & Friston, 2025). In the revised manuscript, we provide a short, high-level overview of these results (in the Introduction form line 55 and in the new section “Theoretical background”, line 128, but also the Mathematical Appendices 1-2 in the Supplementary Material for a comprehensive formal derivation). According to this new theoretical model, attractor states can be understood as a set of priors (in the Bayesian sense) that together constitute an optimal orthogonal basis, equipping the update process (which is akin to a Markov-chain Monte Carlo sampling) to find posteriors that generalize effectively within the spanned subspace. Thus, in sum, understanding brain function in terms of attractor dynamics - instead of PCA-like descriptive projections - provides important links towards a Bayesian interpretation of brain activity. At the same time, the eigenvector-attractor correspondence also explains, why descriptive decomposition approaches, like PCA or ICA are so effective at capturing the dynamics of the system, at the first place.

As presented here, the Hopfield model is excellent in its simplicity and power, and it seems suited to tackle the structure-function relationship with the power of going further to explain task-evoked and clinical data. The work could be strengthened if that was taken into consideration. As such the model would not suffer from circularity problems and it would be possible to claim its mechanistic properties. Furthermore, as mentioned above, in the current setup, the connectivity matrix is based on statistical properties of functional activity amongst regions, and as such it is difficult to talk about a certain mechanism. This contention has for example been addressed in the Cole et al. 2016 paper with the use of a biophysical model linking structure and function, thus strengthening the mechanistic claim of the work.

We agree that investigating how the structural connectome constraints macro-scale dynamics is a crucial next step. Linking our results with the theoretical framework of self-orthogonalizing attractor networks provides a principled approach to this, as the “self-orthogonalizing” learning rule in the accompanying theoretical work provides the opportunity to fit attractor networks with structural constraints to functional data, shedding light on the plastic processes which maintain the observed approximate orthogonality even in the presence of these structural constraints. We have revised the manuscript to clarify that our phenomenological approach is inherently limited in its ability to answer mechanistic questions at the level of biophysical details (lines 894) and discuss this promising direction as follows:

Lines 803:

“A promising application of this is to consider structural brain connectivity (as measured by diffusion MRI) as a sparsity constraint for the coupling weights and then train the fcANN model to match the observed resting-state brain dynamics. If the resulting structural-functional ANN model is able to closely match the observed functional brain substate dynamics, it can be used as a novel approach to quantify and understand the structural functional coupling in the brain”.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) The statistical analyses are poorly described throughout the manuscript. The authors should provide more details on the statistical methods used for each comparison, as well as the corresponding statistics and degrees of freedom, rather than solely reporting p-values.

We thank the reviewer for pointing this out. We have revised the manuscript to include the specific test statistics, precise p-values and raw effect sizes for all reported analyses to ensure full transparency and replicability, see e.g. lines 461, 542, 550, 565, 573, 619, as well as Figures 2-4. Additionally, as absolute effect sizes - in many analyses - do not have a straightforward interpretation, we provided Glass’ Δ, as a standardized effect size measure, expressing the distance of the true observation from the null distribution as a ratio of the null standard deviation.

We have also improved the description of the statistical methods used in the manuscript (lines 1270, 1306, 1339, 1367, 1404) and added two overview tables (Tables 1 and 2) that summarize the methodological approaches and the corresponding null models.

Furthermore, we have fully revised the analysis corresponding to noise optimization. We only retained null model 2 (covariance-matched Gaussian) in the main text and on Figure 3, and moved model 1 (spatial phase randomization) into the Supplementary Material (Supplementary Figure 6) and is less appropriate for this analysis (trivially significant in all cases). Furthermore, as test statistic, no we use a Wasserstein distance between the 122-dimensional empirical and the simulated data (instead of focusing on the 2-dimensional projection). This analysis now directly quantifies the capacity of the fcANN model to capture non-Gaussian conditionals in the data.

(2) The convergence procedure is not clearly explained in the manuscript. Is this an optimization procedure to minimize energy? If so, the authors should provide more details about the optimizer used.

We apologize for the lack of clarity. The convergence is not an optimization procedure per se, in a sense that it does not involve any external optimizer. It is simply the repeated (deterministic) application of the same update rule also known from Hopfield networks or Boltzmann machines. However, as detailed in the accompanying theoretical paper, this update rule (or inference rule) inherently solves and optimization problem: it performs gradient descent on the free energy landscape of the network. As such, it is guaranteed to converge to a local free energy minimum in the deterministic case. We have clarified this process in the Results and Methods sections as follows:

Line 161:

“Inference arises from minimizing free energy with respect to the states \sigma. For a single unit, this yields a local update rule homologous to the relaxation dynamics in Hopfield networks”.

Line 181:

“In the basis framework (Spisak & Friston, 2025), inference is a gradient descent on the variational free energy landscape with respect to the states σ and can be interpreted as a form of approximate Bayesian inference, where the expected value of the state σ_i is interpreted as the posterior mean given the attractor states currently encoded in the network (serving as a macro-scale prior) and the previous state, including external inputs (serving as likelihood in the Bayesian sense)”.

Line 1252:

“As the inference rule was derived as a gradient descent on free energy, iterations monotonically decrease the free energy function and therefore converge to a local free‑energy minimum without any external optimizer. Thus, convergence does not require any optimization procedure with an external optimizer. Instead, it arises as the fixed point of repeated local inference updates, which implement gradient descent on free energy in the deterministic symmetric case.”

(3) In Figure 2G, the beta values range from 0.035 to 0.06, but they are reported as 0.4 in the main text and the Supplementary Figure. Please clarify this discrepancy.

We are grateful to the reviewer for spotting this typo. The correct value for β is 0.04, as reported in the Methods section. We have corrected this inconsistency in the revised manuscript and as well as in Supplementary Figure 5.

(4) Line 174: What type of null model was used to evaluate the impact of the beta values? The authors did not provide details on this anywhere in the manuscript.

We apologize for this omission. The null model is based on permuting the connectome weights while retaining the matrix symmetry, which destroys the specific topological structure but preserves the overall weight distribution. We have now clarified this at multiple places in the revised manuscript (lines 432, Table 1-2, Figure 2), and added new overview tables (Tables 1 and 2) to summarize the methodological approaches and the corresponding null models.

(5) Figure 3B: It appears that the authors only demonstrate the reproducibility of the “internal” attractor across different datasets. What about other states?

Thank you for noticing this. We now visualize all attractor states in Figure 3B (note that these essentially consist of two symmetric pairs).

(6) Figure 3: What does “empirical” represent in Figure 3? Is it PCA? If the “empirical” method, which is a much simpler method, can achieve results similar to those of the fcHNN in terms of state occupancy, distribution, and activity flow, what are the benefits of the proposed method? Furthermore, the authors claim that the explanatory power of the fcHNN is higher than that of the empirical model and shows significant differences. However, from my perspective, this difference is not substantial (37.0% vs. 39.9%). What does this signify, particularly in comparison to PCA?

This is a crucial point that is now a central theme of our revised manuscript. The reviewer is correct that the “empirical” method is PCA. PCA - by identifying variance-heavy orthogonal directions - aims to explain the highest amount of variance possible in the data (with the assumption of Gaussian conditionals). While empirical attractors are closely aligned to the PCs (i.e. eigenvectors of the inverse covariance matrix, as shown in the new analysis Q1), the alignment is only approximate. We basically take advantage of this small “gap” to quantify, weather attractor states are a better fit to the unseen data than the PCs. Obviously, due to the otherwise strong PC-attractor correspondence, this is expected to be only a small improvement. However, it is an important piece of evidence for the validity of our framework, as it shows that attractors are not just a complementary, perhaps “noisier” variety of the PCs, but a “substrate” that generalizes better to unseen data than the PCs themselves. We have revised the manuscript to clarify this point (lines 528).

Reviewer #2 (Recommendations For The Authors):

For clarity, it might be useful to define and use consistently certain key terms. Connectome often refers to structural (anatomical) connectivity unless defined specifically this should be considered, in Figure 1B title for example Brain state often refers to different conditions ie autism, neurotypical, sleep, etc... see for review Kringelbach et al. 2020, Cell Reports. When referring to attractors of brain activity they might be called substates.

We thank the reviewer for these helpful suggestions. We have carefully revised the manuscript to ensure our terminology is precise and consistent. We now explicitly refer to the “functional connectome” (including the title) and avoid using the too general term “brain state” and use “substates” instead.

In Figure 2 some terms are not defined. Noise is sigma in the text but elpsilon in the figure. Only in methods, the link becomes clear. Perhaps define epsilon in the caption for clarity. The same applies to μ in the methods. It is only described above in the methods, I suggest repeating the epsilon definition for clarity

We appreciate this feedback and apologize for the inconsistency. We have revised all figures and the Methods section to ensure that all mathematical symbols (including ε, σ, and μ) are clearly and consistently defined upon their first appearance and in all figure captions. For instance, noise level is now consistently referred to as ϵ. We improved the consistency and clarity for other terms, too, including:

functional connectome-based Hopfiled network (fcHNN) => functional connectivity-based attractor network (fcANN);

temperature => inverse temperature;

And improved grammar and language throughout the manuscript.

References

Kanter, I., & Sompolinsky, H. (1987). Associative recall of memory without errors. Physical Review A, 35(1), 380–392. 10.1103/physreva.35.380

Spisak T & Friston K (2025). Self-orthogonalizing attractor neural networks emerging from the free energy principle. arXiv preprint arXiv:2505.22749.

eLife Assessment

O'Brien and co-authors provide important data demonstrating that tissue-resident macrophages can exert physiological functions and influence endocrine systems.Their model in which AMs negatively regulate aldosterone production via effects exerted in the lung is solid. The work will be of broad interest to cell biologists and immunologists.

Reviewer #2 (Public review):

Summary:

Tissue-resident macrophages are more and more thought to exert key homeostatic functions and contribute to physiological responses. In the report of O'Brien and Colleagues, the idea that the macrophage-expressed scavenger receptor MARCO could regulate adrenal corticosteroid output at steady-state was explored. The authors found that male MARCO-deficient mice exhibited higher plasma aldosterone levels and higher lung ACE expression as compared to wild-type mice, while the availability of cholesterol and the machinery required to produce aldosterone in the adrenal gland were not affected by MARCO deficiency. The authors take these data to conclude that MARCO in alveolar macrophages can negatively regulate ACE expression and aldosterone production at steady-state and that MARCO-deficient mice suffer from a secondary hyperaldosteronism.

Strengths:

If properly demonstrated and validated, the fact that tissue-resident macrophages can exert physiological functions and influence endocrine systems would be highly significant and could be amenable to novel therapies.

Major weakness:

The comparison between C57BL/6J wild-type mice and knock-out mice for which a precise information about the genetic background and the history of breedings and crossings is lacking can lead to misinterpretations of the results obtained. Hence, MARCO-deficient mice should be compared with true littermate controls.

Author response:

The following is the authors’ response to the original reviews

We again thank the reviewers for their comments and recommendations. In response to the reviewer’s suggestions, we have performed several additional experiments, added additional discussion, and updated our conclusions to reflect the additional work. Specifically, we have performed additional analyses in female WT and Marco-deficient animals, demonstrating that the Marco-associated phonotypes observed in male mice (reduced adrenal weight, increased lung Ace mRNA and protein expression, unchanged expression of adrenal corticosteroid biosynthetic enzymes) are not present in female mice. We also report new data on the physiological consequences of increased aldosterone levels observed in male mice, namely plasma sodium and potassium titres, and blood pressure alterations in WT vs Marco-deficient male mice. In an attempt to address the reviewer’s comments relating to our proposed mechanism on the regulation of lung Ace expression, we additionally performed a co-culture experiment using an alveolar macrophage cell line and an endothelial cell line. In light of the additional evidence presented herein, we have updated our conclusions from this study and changed the title of our work to acknowledge that the mechanism underlying the reported phenotype remains incompletely understood. Specific responses to reviewers can be seen below.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The investigators sought to determine whether Marco regulates the levels of aldosterone by limiting uptake of its parent molecule cholesterol in the adrenal gland. Instead, they identify an unexpected role for Marco on alveolar macrophages in lowering the levels of angiotensin-converting enzyme in the lung. This suggests an unexpected role of alveolar macrophages and lung ACE in the production of aldosterone.

Strengths:

The investigators suggest an unexpected role for ACE in the lung in the regulation of systemic aldosterone levels.

The investigators suggest important sex-related differences in the regulation of aldosterone by alveolar macrophages and ACE in the lung.

Studies to exclude a role for Marco in the adrenal gland are strong, suggesting an extra-adrenal source for the excess Marco observed in male Marco knockout mice.

Weaknesses:

While the investigators have identified important sex differences in the regulation of extrapulmonary ACE in the regulation of aldosterone levels, the mechanisms underlying these differences are not explored.

The physiologic impact of the increased aldosterone levels observed in Marco -/- male mice on blood pressure or response to injury is not clear.

The intracellular signaling mechanism linking lung macrophage levels with the expression of ACE in the lung is not supported by direct evidence.

Reviewer #2 (Public Review):

Summary:

Tissue-resident macrophages are more and more thought to exert key homeostatic functions and contribute to physiological responses. In the report of O'Brien and Colleagues, the idea that the macrophage-expressed scavenger receptor MARCO could regulate adrenal corticosteroid output at steady-state was explored. The authors found that male MARCO-deficient mice exhibited higher plasma aldosterone levels and higher lung ACE expression as compared to wild-type mice, while the availability of cholesterol and the machinery required to produce aldosterone in the adrenal gland were not affected by MARCO deficiency. The authors take these data to conclude that MARCO in alveolar macrophages can negatively regulate ACE expression and aldosterone production at steady-state and that MARCO-deficient mice suffer from secondary hyperaldosteronism.

Strengths:

If properly demonstrated and validated, the fact that tissue-resident macrophages can exert physiological functions and influence endocrine systems would be highly significant and could be amenable to novel therapies.

Weaknesses:

The data provided by the authors currently do not support the major claim of the authors that alveolar macrophages, via MARCO, are involved in the regulation of a hormonal output in vivo at steady-state. At this point, there are two interesting but descriptive observations in male, but not female, MARCO-deficient animals, and overall, the study lacks key controls and validation experiments, as detailed below.

Major weaknesses:

(1) According to the reviewer's own experience, the comparison between C57BL/6J wild-type mice and knock-out mice for which precise information about the genetic background and the history of breedings and crossings is lacking, can lead to misinterpretations of the results obtained. Hence, MARCO-deficient mice should be compared with true littermate controls.

(2) The use of mice globally deficient for MARCO combined with the fact that alveolar macrophages produce high levels of MARCO is not sufficient to prove that the phenotype observed is linked to alveolar macrophage-expressed MARCO (see below for suggestions of experiments).

(3) If the hypothesis of the authors is correct, then additional read-outs could be performed to reinforce their claims: levels of Angiotensin I would be lower in MARCO-deficient mice, levels of Antiotensin II would be higher in MARCO-deficient mice, Arterial blood pressure would be higher in MARCO-deficient mice, natremia would be higher in MARCO-deficient mice, while kaliemia would be lower in MARCO-deficient mice. In addition, co-culture experiments between MARCO-sufficient or deficient alveolar macrophages and lung endothelial cells, combined with the assessment of ACE expression, would allow the authors to evaluate whether the AM-expressed MARCO can directly regulate ACE expression.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) Corticosterone levels in male Marco -/- mice are not significantly different, but there is (by eye) substantially more variability in the knockout compared to the wild type. A power analysis should be performed to determine the number of mice needed to detect a similar % difference in corticosterone to the difference observed in aldosterone between male Marco knockout and wild-type mice. If necessary the experiments should be repeated with an adequately powered cohort.

Using a power calculator (www.gigacalculator.com) it was determined that our sample size of 13 was one less than sufficient to detect a similar % difference in corticosterone as was detected in corticosterone. We regret that we unable to perform additional measurements as the author suggested in the available timeframe.

(2) All of the data throughout the MS (particularly data in the lung) should be presented in male and female mice. For example, the induction of ACE in the lungs of Marco-/- female mice should be absent. Similar concerns relate to the dexamethasone suppression studies. Also would be useful if the single cell data could be examined by sex--should be possible even post hoc using Xist etc.

Given the limitations outlined in our previous response to reviewers it was not possible to repeat every experiment from the original manuscript. We were able to measure the expression of lung Ace mRNA, ACE protein, adrenal weights, adrenal expression of steroid biosynthetic enzymes, presence of myeloid cells, and levels of serum electrolytes in female animals. These are presented in figures 1G, 3B, 4A, 4E, 4F, 4I, and 4J. We have elected to not present single cell seq data according to sex as it did not indicate substantial differences between males and females in Marco or Ace expression and so does not substantively change our approach.

(3) IF is notoriously unreliable in the lung, which has high levels of autofluorescence. This is the only method used to show ACE levels are increased in the absence of Marco. Orthogonal methods (e.g. immunoblots of flow-sorted cells, or ideally CITE-seq that includes both male and female mice) should be used.

We used negative controls to guide our settings during acquisition of immunofluorescent images. Additionally, we also used qPCR to show an increase in Ace mRNA expression in the lung in addition to the protein level. This data was presented in the original manuscript and is further bolstered by our additional presentation of expression data for Ace mRNA and protein in female animals in this revised manuscript.

(4) Given the central importance of ACE staining to the conclusions, validation of the antibody should be included in the supplement.

We don’t have ACE-deficient mice so cannot do KO validation of the antibody. We did perform secondary stain controls which confirmed the signal observed is primary antibody-derived. Moreover, we specifically chose an anti-ACE antibody (Invitrogen catalogue # MA5-32741) that has undergone advanced verification with the manufacturer. We additionally tested the antibody in the brain and liver and observed no significant levels of staining.

Author response image 1.

(5) The link between alveolar macrophage Marco and ACE is poorly explored.

We carried out a co-culture experiments of alveolar macrophages and endothelial cells and measure ACE/Ace expression as a consequence. This is presented in figure 5D and the discussion.

(6) Mechanisms explaining the substantial sex difference in the primary outcome are not explored.

This is outside the scope if this project, though we would consider exploring such experiments in future studies.

(7) Are there physiologic consequences either in homeostasis or under stress to the increased aldosterone (or lung ACE levels) observed in Marco-/- male mice?

We measured blood electrolytes and blood pressure in Marco-deficient and Marco-sufficient mice. The results from these experiments are presented in 4G-4M.

Reviewer #2 (Recommendations For The Authors):

Below is a suggestion of important control or validation experiments to be performed in order to support the authors' claims.

(1) It is imperative to validate that the phenotype observed in MARCO-deficient mice is indeed caused by the deficiency in MARCO. To this end, littermate mice issued from the crossing between heterozygous MARCO +/- mice should be compared to each other. C57BL/6J mice can first be crossed with MARCO-deficient mice in F0, and F1 heterozygous MARCO +/- mice should be crossed together to produce F2 MARCO +/+, MARCO +/- and MARCO -/- littermate mice that can be used for experiments.

We thank the reviewer for their comments. We recognise the concern of the reviewer but due to limited experimenter availability we are unable to undertake such a breeding programme to address this particular concern.

(2) The use of mice in which AM, but not other cells, lack MARCO expression would demonstrate that the effect is indeed linked to AM. To this end, AM-deficient Csf2rb-deficient mice could be adoptively transferred with MARCO-deficient AM. In addition, the phenotype of MARCO-deficient mice should be restored by the adoptive transfer of wild-type, MARCO-expressing AM. Alternatively, bone marrow chimeras in which only the hematopoietic compartment is deficient in MARCO would be another option, albeit less specific for AM.

We recognise the concern of the reviewer. We carried out a co-culture experiments of alveolar macrophages and endothelial cells and measure ACE/Ace expression as a consequence. This is presented in figure 5D and the implications explored in the discussion.

(3) If the hypothesis of the authors is correct, then additional read-outs could be performed to reinforce their claims: levels of Angiotensin I would be lower in MARCO-deficient mice, levels of Antiotensin II would be higher in MARCO-deficient mice, Arterial blood pressure would be higher in MARCO-deficient mice, natremia would be higher in MARCO-deficient mice, while kaliemia would be lower in MARCO-deficient mice. Similar read-outs could also be performed in the models proposed in point 2).

We measured blood electrolytes and blood pressure in Marco-deficient and Marco-sufficient mice. The results from these experiments are presented in 4G-4M.

(4) Co-culture experiments between MARCO-sufficient or deficient alveolar macrophages and lung endothelial cells, combined with the assessment of ACE expression, would allow the authors to evaluate whether the AM-expressed MARCO can directly regulate ACE expression.

To address this concern we carried out a co-culture experiment as described above.

这里的每个单元下的笔记数量在预览和创建计划时未体现作用

这里目标出价平台是有限制的，这里没有限制

计划名称处，第一次点击插入更多时，会默认选择时间、编号，去掉默认选择

目前在名称插入通配符时，无法插入到光标指定的位置，只能插到最后面，需要入到光标指定的位置

模版这部分，当光标悬停到特定保存过的模版时，应该能够显示该模版的全称

当自传封面类型选择“笔记全部图片”时，创量会有以下提示：

自传封面类型已选择 “笔记全部图片”，受媒体接口频控限制，可能导致计划创建失败，请注意。

eLife Assessment

This useful study presents Altair-LSFM, a solid and well-documented implementation of a light-sheet fluorescence microscope (LSFM) designed for accessibility and cost reduction. While the approach offers strengths such as the use of custom-machined baseplates and detailed assembly instructions, its overall impact is limited by the lack of live-cell imaging capabilities and the absence of a clear, quantitative comparison to existing LSFM platforms. As such, although technically competent, the broader utility and uptake of this system by the community may be limited.

Reviewer #1 (Public review):

Summary:

The article presents the details of the high-resolution light-sheet microscopy system developed by the group. In addition to presenting the technical details of the system, its resolution has been characterized and its functionality demonstrated by visualizing subcellular structures in a biological sample.

Strengths:

(1) The article includes extensive supplementary material that complements the information in the main article.

(2) However, in some sections, the information provided is somewhat superficial.

Weaknesses:

(1) Although a comparison is made with other light-sheet microscopy systems, the presented system does not represent a significant advance over existing systems. It uses high numerical aperture objectives and Gaussian beams, achieving resolution close to theoretical after deconvolution. The main advantage of the presented system is its ease of construction, thanks to the design of a perforated base plate.

(2) Using similar objectives (Nikon 25x and Thorlabs 20x), the results obtained are similar to those of the LLSM system (using a Gaussian beam without laser modulation). However, the article does not mention the difficulties of mounting the sample in the implemented configuration.

(3) The authors present a low-cost, open-source system. Although they provide open source code for the software (navigate), the use of proprietary electronics (ASI, NI, etc.) makes the system relatively expensive. Its low cost is not justified.

(4) The fibroblast images provided are of exceptional quality. However, these are fixed samples. The system lacks the necessary elements for monitoring cells in vivo, such as temperature or pH control.

Reviewer #2 (Public review):

Summary:

The authors present Altair-LSFM (Light Sheet Fluorescence Microscope), a high-resolution, open-source microscope, that is relatively easy to align and construct and achieves sub-cellular resolution. The authors developed this microscope to fill a perceived need that current open-source systems are primarily designed for large specimens and lack sub-cellular resolution or are difficult to construct and align, and are not stable. While commercial alternatives exist that offer sub-cellular resolution, they are expensive. The authors' manuscript centers around comparisons to the highly successful lattice light-sheet microscope, including the choice of detection and excitation objectives. The authors thus claim that there remains a critical need for high-resolution, economical, and easy-to-implement LSFM systems.

Strengths:

The authors succeed in their goals of implementing a relatively low-cost (~ USD 150K) open-source microscope that is easy to align. The ease of alignment rests on using custom-designed baseplates with dowel pins for precise positioning of optics based on computer analysis of opto-mechanical tolerances, as well as the optical path design. They simplify the excitation optics over Lattice light-sheet microscopes by using a Gaussian beam for illumination while maintaining lateral and axial resolutions of 235 and 350 nm across a 260-um field of view after deconvolution. In doing so they rest on foundational principles of optical microscopy that what matters for lateral resolution is the numerical aperture of the detection objective and proper sampling of the image field on to the detection, and the axial resolution depends on the thickness of the light-sheet when it is thinner than the depth of field of the detection objective. This concept has unfortunately not been completely clear to users of high-resolution light-sheet microscopes and is thus a valuable demonstration. The microscope is controlled by an open-source software, Navigate, developed by the authors, and it is thus foreseeable that different versions of this system could be implemented depending on experimental needs while maintaining easy alignment and low cost. They demonstrate system performance successfully by characterizing their sheet, point-spread function, and visualization of sub-cellular structures in mammalian cells, including microtubules, actin filaments, nuclei, and the Golgi apparatus.

Weaknesses:

There is a fixation on comparison to the first-generation lattice light-sheet microscope, which has evolved significantly since then:

(1) The authors claim that commercial lattice light-sheet microscopes (LLSM) are "complex, expensive, and alignment intensive", I believe this sentence applies to the open-source version of LLSM, which was made available for wide dissemination. Since then, a commercial solution has been provided by 3i, which is now being used in multiple cores and labs but does require routine alignments. However, Zeiss has also released a commercial turn-key system, which, while expensive, is stable, and the complexity does not interfere with the experience of the user. Though in general, statements on ease of use and stability might be considered anecdotal and may not belong in a scientific article, unreferenced or without data.

(2) One of the major limitations of the first generation LLSM was the use of a 5 mm coverslip, which was a hinderance for many users. However, the Zeiss system elegantly solves this problem, and so does Oblique Plane Microscopy (OPM), while the Altair-LSFM retains this feature, which may dissuade widespread adoption. This limitation and how it may be overcome in future iterations is not discussed.

(3) Further, on the point of sample flexibility, all generations of the LLSM, and by the nature of its design, the OPM, can accommodate live-cell imaging with temperature, gas, and humidity control. It is unclear how this would be implemented with the current sample chamber. This limitation would severely limit use cases for cell biologists, for which this microscope is designed. There is no discussion on this limitation or how it may be overcome in future iterations.

(4) The authors' comparison to LLSM is constrained to the "square" lattice, which, as they point out, is the most used optical lattice (though this also might be considered anecdotal). The LLSM original design, however, goes far beyond the square lattice, including hexagonal lattices, the ability to do structured illumination, and greater flexibility in general in terms of light-sheet tuning for different experimental needs, as well as not being limited to just sample scanning. Thus, the Alstair-LSFM cannot compare to the original LLSM in terms of versatility, even if comparisons to the resolution provided by the square lattice are fair.

(5) There is no demonstration of the system's live-imaging capabilities or temporal resolution, which is the main advantage of existing light-sheet systems.

While the microscope is well designed and completely open source, it will require experience with optics, electronics, and microscopy to implement and align properly. Experience with custom machining or soliciting a machine shop is also necessary. Thus, in my opinion, it is unlikely to be implemented by a lab that has zero prior experience with custom optics or can hire someone who does. Altair-LSFM may not be as easily adaptable or implementable as the authors describe or perceive in any lab that is interested, even if they can afford it. The authors indicate they will offer "workshops," but this does not necessarily remove the barrier to entry or lower it, perhaps as significantly as the authors describe.

There is a claim that this design is easily adaptable. However, the requirement of custom-machined baseplates and in silico optimization of the optical path basically means that each new instrument is a new design, even if the Navigate software can be used. It is unclear how Altair-LSFM demonstrates a modular design that reduces times from conception to optimization compared to previous implementations.

Reviewer #3 (Public review):

Summary:

This manuscript introduces a high-resolution, open-source light-sheet fluorescence microscope optimized for sub-cellular imaging.

The system is designed for ease of assembly and use, incorporating a custom-machined baseplate and in silico optimized optical paths to ensure robust alignment and performance. The authors demonstrate lateral and axial resolutions of ~235 nm and ~350 nm after deconvolution, enabling imaging of sub-diffraction structures in mammalian cells.

The important feature of the microscope is the clever and elegant adaptation of simple gaussian beams, smart beam shaping, galvo pivoting and high NA objectives to ensure a uniform thin light-sheet of around 400 nm in thickness, over a 266 micron wide Field of view, pushing the axial resolution of the system beyond the regular diffraction limited-based tradeoffs of light-sheet fluorescence microscopy.

Compelling validation using fluorescent beads and multicolor cellular imaging highlights the system's performance and accessibility. Moreover, a very extensive and comprehensive manual of operation is provided in the form of supplementary materials. This provides a DIY blueprint for researchers who want to implement such a system.

Strengths:

(1) Strong and accessible technical innovation:

With an elegant combination of beam shaping and optical modelling, the authors provide a high-resolution light-sheet system that overcomes the classical light-sheet tradeoff limit of a thin light-sheet and a small field of view. In addition, the integration of in silico modelling with a custom-machined baseplate is very practical and allows for ease of alignment procedures. Combining these features with the solid and super-extensive guide provided in the supplementary information, this provides a protocol for replicating the microscope in any other lab.

(2) Impeccable optical performance and ease of mounting of samples:

The system takes advantage of the same sample-holding method seen already in other implementations, but reduces the optical complexity. At the same time, the authors claim to achieve similar lateral and axial resolution to Lattice-light-sheet microscopy (although without a direct comparison (see below in the "weaknesses" section). The optical characterization of the system is comprehensive and well-detailed. Additionally, the authors validate the system imaging sub-cellular structures in mammalian cells.

(3) Transparency and comprehensiveness of documentation and resources:

A very detailed protocol provides detailed documentation about the setup, the optical modeling, and the total cost.

Weaknesses:

(1) Limited quantitative comparisons:

Although some qualitative comparison with previously published systems (diSPIM, lattice light-sheet) is provided throughout the manuscript, some side-by-side comparison would be of great benefit for the manuscript, even in the form of a theoretical simulation. While having a direct imaging comparison would be ideal, it's understandable that this goes beyond the interest of the paper; however, a table referencing image quality parameters (taken from the literature), such as signal-to-noise ratio, light-sheet thickness, and resolutions, would really enhance the features of the setup presented. Moreover, based also on the necessity for optical simplification, an additional comment on the importance/difference of dual objective/single objective light-sheet systems could really benefit the discussion.

(2) Limitation to a fixed sample:

In the manuscript, there is no mention of incubation temperature, CO₂ regulation, Humidity control, or possible integration of commercial environmental control systems. This is a major limitation for an imaging technique that owes its popularity to fast, volumetric, live-cell imaging of biological samples.

(3) System cost and data storage cost:

While the system presented has the advantage of being open-source, it remains relatively expensive (considering the 150k without laser source and optical table, for example). The manuscript could benefit from a more direct comparison of the performance/cost ratio of existing systems, considering academic settings with budgets that most of the time would not allow for expensive architectures. Moreover, it would also be beneficial to discuss the adaptability of the system, in case a 30k objective could not be feasible. Will this system work with different optics (with the obvious limitations coming with the lower NA objective)? This could be an interesting point of discussion. Adaptability of the system in case of lower budgets or more cost-effective choices, depending on the needs.

Last, not much is said about the need for data storage. Light-sheet microscopy's bottleneck is the creation of increasingly large datasets, and it could be beneficial to discuss more about the storage needs and the quantity of data generated.

Conclusion:

Altair-LSFM represents a well-engineered and accessible light-sheet system that addresses a longstanding need for high-resolution, reproducible, and affordable sub-cellular light-sheet imaging. While some aspects-comparative benchmarking and validation, limitation for fixed samples-would benefit from further development, the manuscript makes a compelling case for Altair-LSFM as a valuable contribution to the open microscopy scientific community.

Author response:

The following is the authors’ response to the original reviews.

eLife Assessment

This useful study presents Altair-LSFM, a solid and well-documented implementation of a light-sheet fluorescence microscope (LSFM) designed for accessibility and cost reduction. While the approach offers strengths such as the use of custom-machined baseplates and detailed assembly instructions, its overall impact is limited by the lack of live-cell imaging capabilities and the absence of a clear, quantitative comparison to existing LSFM platforms. As such, although technically competent, the broader utility and uptake of this system by the community may be limited.

We thank the editors and reviewers for their thoughtful evaluation of our work and for recognizing the technical strengths of the Altair-LSFM platform, including the custom-machined baseplates and detailed documentation provided to promote accessibility and reproducibility. Below, we provide point-by-point responses to each referee comment. In the process, we have significantly revised the manuscript to include live-cell imaging data and a quantitative evaluation of imaging speed. We now more explicitly describe the different variants of lattice light-sheet microscopy—highlighting differences in their illumination flexibility and image acquisition modes—and clarify how Altair-LSFM compares to each. We further discuss challenges associated with the 5 mm coverslip and propose practical strategies to overcome them. Additionally, we outline cost-reduction opportunities, explain the rationale behind key equipment selections, and provide guidance for implementing environmental control. Altogether, we believe these additions have strengthened the manuscript and clarified both the capabilities and limitations of AltairLSFM.

Public Reviews:

Reviewer #1 (Public review): 

Summary: 

The article presents the details of the high-resolution light-sheet microscopy system developed by the group. In addition to presenting the technical details of the system, its resolution has been characterized and its functionality demonstrated by visualizing subcellular structures in a biological sample.

Strengths: 

(1) The article includes extensive supplementary material that complements the information in the main article.

(2) However, in some sections, the information provided is somewhat superficial.

We thank the reviewer for their thoughtful assessment and for recognizing the strengths of our manuscript, including the extensive supplementary material. Our goal was to make the supplemental content as comprehensive and useful as possible. In addition to the materials provided with the manuscript, our intention is for the online documentation (available at thedeanlab.github.io/altair) to serve as a living resource that evolves in response to user feedback. We would therefore greatly appreciate the reviewer’s guidance on which sections were perceived as superficial so that we can expand them to better support readers and builders of the system.

Weaknesses:

(1) Although a comparison is made with other light-sheet microscopy systems, the presented system does not represent a significant advance over existing systems. It uses high numerical aperture objectives and Gaussian beams, achieving resolution close to theoretical after deconvolution. The main advantage of the presented system is its ease of construction, thanks to the design of a perforated base plate.

We appreciate the reviewer’s assessment and the opportunity to clarify our intent. Our primary goal was not to introduce new optical functionality beyond that of existing high-performance light-sheet systems, but rather to substantially reduce the barrier to entry for non-specialist laboratories. Many open-source implementations, such as OpenSPIM, OpenSPIN, and Benchtop mesoSPIM, similarly focused on accessibility and reproducibility rather than introducing new optical modalities, yet have had a measureable impact on the field by enabling broader community participation. Altair-LSFM follows this tradition, providing sub-cellular resolution performance comparable to advanced systems like LLSM, while emphasizing reproducibility, ease of construction through a precision-machined baseplate, and comprehensive documentation to facilitate dissemination and adoption.

(2) Using similar objectives (Nikon 25x and Thorlabs 20x), the results obtained are similar to those of the LLSM system (using a Gaussian beam without laser modulation). However, the article does not mention the difficulties of mounting the sample in the implemented configuration.

We appreciate the reviewer’s comment and agree that there are practical challenges associated with handling 5 mm diameter coverslips in this configuration. In the revised manuscript, we now explicitly describe these challenges and provide practical solutions. Specifically, we highlight the use of a custommachined coverslip holder designed to simplify mounting and handling, and we direct readers to an alternative configuration using the Zeiss W Plan-Apochromat 20×/1.0 objective, which eliminates the need for small coverslips altogether.

(3) The authors present a low-cost, open-source system. Although they provide open source code for the software (navigate), the use of proprietary electronics (ASI, NI, etc.) makes the system relatively expensive. Its low cost is not justified.

We appreciate the reviewer’s perspective and understand the concern regarding the use of proprietary control hardware such as the ASI Tiger Controller and NI data acquisition cards. Our decision to use these components was intentional: relying on a unified, professionally supported and maintained platform minimizes complexity associated with sourcing, configuring, and integrating hardware from multiple vendors, thereby reducing non-financial barriers to entry for non-specialist users.

Importantly, these components are not the primary cost driver of Altair-LSFM (they represent roughly 18% of the total system cost). Nonetheless, for individuals where the price is prohibitive, we also outline several viable cost-reduction options in the revised manuscript (e.g., substituting manual stages, omitting the filter wheel, or using industrial CMOS cameras), while discussing the trade-offs these substitutions introduce in performance and usability. These considerations are now summarized in Supplementary Note 1, which provides a transparent rationale for our design and cost decisions.

Finally, we note that even with these professional-grade components, Altair-LSFM remains substantially less expensive than commercial systems offering comparable optical performance, such as LLSM implementations from Zeiss or 3i.

(4) The fibroblast images provided are of exceptional quality. However, these are fixed samples. The system lacks the necessary elements for monitoring cells in vivo, such as temperature or pH control.

We thank the reviewer for their positive comment regarding the quality of our data. As noted, the current manuscript focuses on validating the optical performance and resolution of the system using fixed specimens to ensure reproducibility and stability.

We fully agree on the importance of environmental control for live-cell imaging. In the revised manuscript, we now describe in detail how temperature regulation can be achieved using a custom-designed heated sample chamber, accompanied by detailed assembly instructions on our GitHub repository and summarized in Supplementary Note 2. For pH stabilization in systems lacking a 5% CO₂ atmosphere, we recommend supplementing the imaging medium with 10–25 mM HEPES buffer. Additionally, we include new live-cell imaging data demonstrating that Altair-LSFM supports in vitro time-lapse imaging of dynamic cellular processes under controlled temperature conditions.

Reviewer #2 (Public review): 

Summary: 

The authors present Altair-LSFM (Light Sheet Fluorescence Microscope), a high-resolution, open-source microscope, that is relatively easy to align and construct and achieves sub-cellular resolution. The authors developed this microscope to fill a perceived need that current open-source systems are primarily designed for large specimens and lack sub-cellular resolution or are difficult to construct and align, and are not stable. While commercial alternatives exist that offer sub-cellular resolution, they are expensive. The authors' manuscript centers around comparisons to the highly successful lattice light-sheet microscope, including the choice of detection and excitation objectives. The authors thus claim that there remains a critical need for high-resolution, economical, and easy-to-implement LSFM systems. 

We thank the reviewer for their thoughtful summary. We agree that existing open-source systems primarily emphasize imaging of large specimens, whereas commercial systems that achieve sub-cellular resolution remain costly and complex. Our aim with Altair-LSFM was to bridge this gap—providing LLSM-level performance in a substantially more accessible and reproducible format. By combining high-NA optics with a precision-machined baseplate and open-source documentation, Altair offers a practical, high-resolution solution that can be readily adopted by non-specialist laboratories.

Strengths: 

The authors succeed in their goals of implementing a relatively low-cost (~ USD 150K) open-source microscope that is easy to align. The ease of alignment rests on using custom-designed baseplates with dowel pins for precise positioning of optics based on computer analysis of opto-mechanical tolerances, as well as the optical path design. They simplify the excitation optics over Lattice light-sheet microscopes by using a Gaussian beam for illumination while maintaining lateral and axial resolutions of 235 and 350 nm across a 260-um field of view after deconvolution. In doing so they rest on foundational principles of optical microscopy that what matters for lateral resolution is the numerical aperture of the detection objective and proper sampling of the image field on to the detection, and the axial resolution depends on the thickness of the light-sheet when it is thinner than the depth of field of the detection objective. This concept has unfortunately not been completely clear to users of high-resolution light-sheet microscopes and is thus a valuable demonstration. The microscope is controlled by an open-source software, Navigate, developed by the authors, and it is thus foreseeable that different versions of this system could be implemented depending on experimental needs while maintaining easy alignment and low cost. They demonstrate system performance successfully by characterizing their sheet, point-spread function, and visualization of sub-cellular structures in mammalian cells, including microtubules, actin filaments, nuclei, and the Golgi apparatus.

We thank the reviewer for their thoughtful and generous assessment of our work. We are pleased that the manuscript’s emphasis on fundamental optical principles, design rationale, and practical implementation was clearly conveyed. We agree that Altair’s modular and accessible architecture provides a strong foundation for future variants tailored to specific experimental needs. To facilitate this, we have made all Zemax simulations, CAD files, and build documentation openly available on our GitHub repository, enabling users to adapt and extend the system for diverse imaging applications.

Weaknesses:

There is a fixation on comparison to the first-generation lattice light-sheet microscope, which has evolved significantly since then:

(1) The authors claim that commercial lattice light-sheet microscopes (LLSM) are "complex, expensive, and alignment intensive", I believe this sentence applies to the open-source version of LLSM, which was made available for wide dissemination. Since then, a commercial solution has been provided by 3i, which is now being used in multiple cores and labs but does require routine alignments. However, Zeiss has also released a commercial turn-key system, which, while expensive, is stable, and the complexity does not interfere with the experience of the user. Though in general, statements on ease of use and stability might be considered anecdotal and may not belong in a scientific article, unreferenced or without data.

We thank the reviewer for this thoughtful and constructive comment. We have revised the manuscript to more clearly distinguish between the original open-source implementation of LLSM and subsequent commercial versions by 3i and ZEISS. The revised Introduction and Discussion now explicitly note that while open-source and early implementations of LLSM can require expert alignment and maintenance, commercial systems—particularly the ZEISS Lattice Lightsheet 7—are designed for automated operation and stable, turn-key use, albeit at higher cost and with limited modifiability. We have also moderated earlier language regarding usability and stability to avoid anecdotal phrasing.

We also now provide a more objective proxy for system complexity: the number of optical elements that require precise alignment during assembly and maintenance thereafter. The original open-source LLSM setup includes approximately 29 optical components that must each be carefully positioned laterally, angularly, and coaxially along the optical path. In contrast, the first-generation Altair-LSFM system contains only nine such elements. By this metric, Altair-LSFM is considerably simpler to assemble and align, supporting our overarching goal of making high-resolution light-sheet imaging more accessible to non-specialist laboratories.

(2) One of the major limitations of the first generation LLSM was the use of a 5 mm coverslip, which was a hinderance for many users. However, the Zeiss system elegantly solves this problem, and so does Oblique Plane Microscopy (OPM), while the Altair-LSFM retains this feature, which may dissuade widespread adoption. This limitation and how it may be overcome in future iterations is not discussed.

We thank the reviewer for this helpful comment. We agree that the use of 5 mm diameter coverslips, while enabling high-NA imaging in the current Altair-LSFM configuration, may pose a practical limitation for some users. We now discuss this more explicitly in the revised manuscript. Specifically, we note that replacing the detection objective provides a straightforward solution to this constraint. For example, as demonstrated by Moore et al. (Lab Chip, 2021), pairing the Zeiss W Plan-Apochromat 20×/1.0 detection objective with the Thorlabs TL20X-MPL illumination objective allows imaging beyond the physical surfaces of both objectives, eliminating the need for small-format coverslips. In the revised text, we propose this modification as an accessible path toward greater compatibility with conventional sample mounting formats. We also note in the Discussion that Oblique Plane Microscopy (OPM) inherently avoids such nonstandard mounting requirements and, owing to its single-objective architecture, is fully compatible with standard environmental chambers.

(3) Further, on the point of sample flexibility, all generations of the LLSM, and by the nature of its design, the OPM, can accommodate live-cell imaging with temperature, gas, and humidity control. It is unclear how this would be implemented with the current sample chamber. This limitation would severely limit use cases for cell biologists, for which this microscope is designed. There is no discussion on this limitation or how it may be overcome in future iterations.

We thank the reviewer for this important observation and agree that environmental control is critical for live-cell imaging applications. It is worth noting that the original open-source LLSM design, as well as the commercial version developed by 3i, provided temperature regulation but did not include integrated control of CO2 or humidity. Despite this limitation, these systems have been widely adopted and have generated significant biological insights. We also acknowledge that both OPM and the ZEISS implementation of LLSM offer clear advantages in this respect, providing compatibility with standard commercial environmental chambers that support full regulation of temperature, CO₂, and humidity.

In the revised manuscript, we expand our discussion of environmental control in Supplementary Note 2, where we describe the Altair-LSFM chamber design in more detail and discuss its current implementation of temperature regulation and HEPES-based pH stabilization. Additionally, the Discussion now explicitly notes that OPM avoids the challenges associated with non-standard sample mounting and is inherently compatible with conventional environmental enclosures.

(4) The authors' comparison to LLSM is constrained to the "square" lattice, which, as they point out, is the most used optical lattice (though this also might be considered anecdotal). The LLSM original design, however, goes far beyond the square lattice, including hexagonal lattices, the ability to do structured illumination, and greater flexibility in general in terms of light-sheet tuning for different experimental needs, as well as not being limited to just sample scanning. Thus, the Alstair-LSFM cannot compare to the original LLSM in terms of versatility, even if comparisons to the resolution provided by the square lattice are fair.

We agree that the original LLSM design offers substantially greater flexibility than what is reflected in our initial comparison, including the ability to generate multiple lattice geometries (e.g., square and hexagonal), operate in structured illumination mode, and acquire volumes using both sample- and lightsheet–scanning strategies. To address this, we now include Supplementary Note 3 that provides a detailed overview of the illumination modes and imaging flexibility afforded by the original LLSM implementation, and how these capabilities compare to both the commercial ZEISS Lattice Lightsheet 7 and our AltairLSFM system. In addition, we have revised the discussion to explicitly acknowledge that the original LLSM could operate in alternative scan strategies beyond sample scanning, providing greater context for readers and ensuring a more balanced comparison.

(5) There is no demonstration of the system's live-imaging capabilities or temporal resolution, which is the main advantage of existing light-sheet systems.

In the revised manuscript, we now include a demonstration of live-cell imaging to directly validate AltairLSFM’s suitability for dynamic biological applications. We also explicitly discuss the temporal resolution of the system in the main text (see Optoelectronic Design of Altair-LSFM), where we detail both software- and hardware-related limitations. Specifically, we evaluate the maximum imaging speed achievable with Altair-LSFM in conjunction with our open-source control software, navigate.

For simplicity and reduced optoelectronic complexity, the current implementation powers the piezo through the ASI Tiger Controller, which modestly reduces its bandwidth. Nonetheless, for a 100 µm stroke typical of light-sheet imaging, we achieved sufficient performance to support volumetric imaging at most biologically relevant timescales. These results, along with additional discussion of the design trade-offs and performance considerations, are now included in the revised manuscript and expanded upon in the supplementary material.

While the microscope is well designed and completely open source, it will require experience with optics, electronics, and microscopy to implement and align properly. Experience with custom machining or soliciting a machine shop is also necessary. Thus, in my opinion, it is unlikely to be implemented by a lab that has zero prior experience with custom optics or can hire someone who does. Altair-LSFM may not be as easily adaptable or implementable as the authors describe or perceive in any lab that is interested, even if they can afford it. The authors indicate they will offer "workshops," but this does not necessarily remove the barrier to entry or lower it, perhaps as significantly as the authors describe.

We appreciate the reviewer’s perspective and agree that building any high-performance custom microscope—Altair-LSFM included—requires a basic understanding of (or willingness to learn) optics, electronics, and instrumentation. Such a barrier exists for all open-source microscopes, and our goal is not to eliminate this requirement entirely but to substantially reduce the technical and logistical challenges that typically accompany the construction of custom light-sheet systems.

Importantly, no machining experience or in-house fabrication capabilities are required. Users can simply submit the provided CAD design files and specifications directly to commercial vendors for fabrication. We have made this process as straightforward as possible by supplying detailed build instructions, recommended materials, and vendor-ready files through our GitHub repository. Our dissemination strategy draws inspiration from other successful open-source projects such as mesoSPIM, which has seen widespread adoption—over 30 implementations worldwide—through a similar model of exhaustive documentation, open-source software, and community support via user meetings and workshops.

We also recognize that documentation alone cannot fully replace hands-on experience. To further lower barriers to adoption, we are actively working with commercial vendors to streamline procurement and assembly, and Altair-LSFM is supported by a Biomedical Technology Development and Dissemination (BTDD) grant that provides resources for hosting workshops, offering real-time community support, and developing supplementary training materials.

In the revised manuscript, we now expand the Discussion to explicitly acknowledge these implementation considerations and to outline our ongoing efforts to support a broad and diverse user base, ensuring that laboratories with varying levels of technical expertise can successfully adopt and maintain the Altair-LSFM platform.

There is a claim that this design is easily adaptable. However, the requirement of custom-machined baseplates and in silico optimization of the optical path basically means that each new instrument is a new design, even if the Navigate software can be used. It is unclear how Altair-LSFM demonstrates a modular design that reduces times from conception to optimization compared to previous implementations.

We thank the reviewer for this insightful comment and agree that our original language regarding adaptability may have overstated the degree to which Altair-LSFM can be modified without prior experience. It was not our intention to imply that the system can be easily redesigned by users with limited technical background. Meaningful adaptations of the optical or mechanical design do require expertise in optical layout, optomechanical design, and alignment.

That said, for laboratories with such expertise, we aim to facilitate modifications by providing comprehensive resources—including detailed Zemax simulations, complete CAD models, and alignment documentation. These materials are intended to reduce the development burden for expert users seeking to tailor the system to specific experimental requirements, without necessitating a complete re-optimization of the optical path from first principles.

In the revised manuscript, we clarify this point and temper our language regarding adaptability to better reflect the realistic scope of customization. Specifically, we now state in the Discussion: “For expert users who wish to tailor the instrument, we also provide all Zemax illumination-path simulations and CAD files, along with step-by-step optimization protocols, enabling modification and re-optimization of the optical system as needed.” This revision ensures that readers clearly understand that Altair-LSFM is designed for reproducibility and straightforward assembly in its default configuration, while still offering the flexibility for modification by experienced users.

Reviewer #3 (Public review):

Summary: 

This manuscript introduces a high-resolution, open-source light-sheet fluorescence microscope optimized for sub-cellular imaging. The system is designed for ease of assembly and use, incorporating a custommachined baseplate and in silico optimized optical paths to ensure robust alignment and performance. The authors demonstrate lateral and axial resolutions of ~235 nm and ~350 nm after deconvolution, enabling imaging of sub-diffraction structures in mammalian cells. The important feature of the microscope is the clever and elegant adaptation of simple gaussian beams, smart beam shaping, galvo pivoting and high NA objectives to ensure a uniform thin light-sheet of around 400 nm in thickness, over a 266 micron wide Field of view, pushing the axial resolution of the system beyond the regular diffraction limited-based tradeoffs of light-sheet fluorescence microscopy. Compelling validation using fluorescent beads and multicolor cellular imaging highlights the system's performance and accessibility. Moreover, a very extensive and comprehensive manual of operation is provided in the form of supplementary materials. This provides a DIY blueprint for researchers who want to implement such a system.

We thank the reviewer for their thoughtful and positive assessment of our work. We appreciate their recognition of Altair-LSFM’s design and performance, including its ability to achieve high-resolution, imaging throughout a 266-micron field of view. While Altair-LSFM approaches the practical limits of diffraction-limited performance, it does not exceed the fundamental diffraction limit; rather, it achieves near-theoretical resolution through careful optical optimization, beam shaping, and alignment. We are grateful for the reviewer’s acknowledgment of the accessibility and comprehensive documentation that make this system broadly implementable.

Strengths:

(1) Strong and accessible technical innovation: With an elegant combination of beam shaping and optical modelling, the authors provide a high-resolution light-sheet system that overcomes the classical light-sheet tradeoff limit of a thin light-sheet and a small field of view. In addition, the integration of in silico modelling with a custom-machined baseplate is very practical and allows for ease of alignment procedures. Combining these features with the solid and super-extensive guide provided in the supplementary information, this provides a protocol for replicating the microscope in any other lab.

(2) Impeccable optical performance and ease of mounting of samples: The system takes advantage of the same sample-holding method seen already in other implementations, but reduces the optical complexity.

At the same time, the authors claim to achieve similar lateral and axial resolution to Lattice-light-sheet microscopy (although without a direct comparison (see below in the "weaknesses" section). The optical characterization of the system is comprehensive and well-detailed. Additionally, the authors validate the system imaging sub-cellular structures in mammalian cells.

(3) Transparency and comprehensiveness of documentation and resources: A very detailed protocol provides detailed documentation about the setup, the optical modeling, and the total cost.

We thank the reviewer for their thoughtful and encouraging comments. We are pleased that the technical innovation, optical performance, and accessibility of Altair-LSFM were recognized. Our goal from the outset was to develop a diffraction-limited, high-resolution light-sheet system that balances optical performance with reproducibility and ease of implementation. We are also pleased that the use of precisionmachined baseplates was recognized as a practical and effective strategy for achieving performance while maintaining ease of assembly.

Weaknesses: 

(1) Limited quantitative comparisons: Although some qualitative comparison with previously published systems (diSPIM, lattice light-sheet) is provided throughout the manuscript, some side-by-side comparison would be of great benefit for the manuscript, even in the form of a theoretical simulation. While having a direct imaging comparison would be ideal, it's understandable that this goes beyond the interest of the paper; however, a table referencing image quality parameters (taken from the literature), such as signalto-noise ratio, light-sheet thickness, and resolutions, would really enhance the features of the setup presented. Moreover, based also on the necessity for optical simplification, an additional comment on the importance/difference of dual objective/single objective light-sheet systems could really benefit the discussion.

In the revised manuscript, we have significantly expanded our discussion of different light-sheet systems to provide clearer quantitative and conceptual context for Altair-LSFM. These comparisons are based on values reported in the literature, as we do not have access to many of these instruments (e.g., DaXi, diSPIM, or commercial and open-source variants of LLSM), and a direct experimental comparison is beyond the scope of this work.

We note that while quantitative parameters such as signal-to-noise ratio are important, they are highly sample-dependent and strongly influenced by imaging conditions, including fluorophore brightness, camera characteristics, and filter bandpass selection. For this reason, we limited our comparison to more general image-quality metrics—such as light-sheet thickness, resolution, and field of view—that can be reliably compared across systems.

Finally, per the reviewer’s recommendation, we have added additional discussion clarifying the differences between dual-objective and single-objective light-sheet architectures, outlining their respective strengths, limitations, and suitability for different experimental contexts.

(2) Limitation to a fixed sample: In the manuscript, there is no mention of incubation temperature, CO₂ regulation, Humidity control, or possible integration of commercial environmental control systems. This is a major limitation for an imaging technique that owes its popularity to fast, volumetric, live-cell imaging of biological samples.

We fully agree that environmental control is critical for live-cell imaging applications. In the revised manuscript, we now describe the design and implementation of a temperature-regulated sample chamber in Supplementary Note 2, which maintains stable imaging conditions through the use of integrated heating elements and thermocouples. This approach enables precise temperature control while minimizing thermal gradients and optical drift. For pH stabilization, we recommend the use of 10–25 mM HEPES in place of CO₂ regulation, consistent with established practice for most light-sheet systems, including the initial variant of LLSM. Although full humidity and CO₂ control are not readily implemented in dual-objective configurations, we note that single-objective designs such as OPM are inherently compatible with commercial environmental chambers and avoid these constraints. Together, these additions clarify how environmental control can be achieved within Altair-LSFM and situate its capabilities within the broader LSFM design space.

(3) System cost and data storage cost: While the system presented has the advantage of being opensource, it remains relatively expensive (considering the 150k without laser source and optical table, for example). The manuscript could benefit from a more direct comparison of the performance/cost ratio of existing systems, considering academic settings with budgets that most of the time would not allow for expensive architectures. Moreover, it would also be beneficial to discuss the adaptability of the system, in case a 30k objective could not be feasible. Will this system work with different optics (with the obvious limitations coming with the lower NA objective)? This could be an interesting point of discussion. Adaptability of the system in case of lower budgets or more cost-effective choices, depending on the needs.

We agree that cost considerations are critical for adoption in academic environments. We would also like to clarify that the quoted $150k includes the optical table and laser source. In the revised manuscript, Supplementary Note 1 now includes an expanded discussion of cost–performance trade-offs and potential paths for cost reduction.

Last, not much is said about the need for data storage. Light-sheet microscopy's bottleneck is the creation of increasingly large datasets, and it could be beneficial to discuss more about the storage needs and the quantity of data generated.

In the revised manuscript, we now include Supplementary Note 4, which provides a high-level discussion of data storage needs, approximate costs, and practical strategies for managing large datasets generated by light-sheet microscopy. This section offers general guidance—including file-format recommendations, and cost considerations—but we note that actual costs will vary by institution and contractual agreements.

Conclusion:

Altair-LSFM represents a well-engineered and accessible light-sheet system that addresses a longstanding need for high-resolution, reproducible, and affordable sub-cellular light-sheet imaging. While some aspects-comparative benchmarking and validation, limitation for fixed samples-would benefit from further development, the manuscript makes a compelling case for Altair-LSFM as a valuable contribution to the open microscopy scientific community. 

Recommendations for the authors:

Reviewer #2 (Recommendations for the authors):

(1) A picture, or full CAD design of the complete instrument, should be included as a main figure.

A complete CAD rendering of the microscope is now provided in Supplementary Figure 4.

(2) There is no quantitative comparison of the effects of the tilting resonant galvo; only a cartoon, a figure should be included.

The cartoon was intended purely as an educational illustration to conceptually explain the role of the tilting resonant galvo in shaping and homogenizing the light sheet. To clarify this intent, we have revised both the figure legend and corresponding text in the main manuscript. For readers seeking quantitative comparisons, we now reference the original study that provides a detailed analysis of this optical approach, as well as a review on the subject.

(3) Description of L4 is missing in the Figure 1 caption.

Thank you for catching this omission. We have corrected it.

(4) The beam profiles in Figures 1c and 3a, please crop and make the image bigger so the profile can be appreciated. The PSFs in Figure 3c-e should similarly be enlarged and presented using a dynamic range/LUT such that any aberrations can be appreciated.

In Figure 1c, our goal was to qualitatively illustrate the uniformity of the light-sheet across the full field of view, while Figure 1d provided the corresponding quantitative cross-section. To improve clarity, we have added an additional figure panel offering a higher-magnification, localized view of the light-sheet profile. For Figure 3c–e, we have enlarged the PSF images and adjusted the display range to better convey the underlying signal and allow subtle aberrations to be appreciated.

(5) It is unclear why LLSM is being used as the gold standard, since in its current commercial form, available from Zeiss, it is a turn-key system designed for core facilities. The original LLSM is also a versatile instrument that provides much more than the square lattice for illumination, including structured illumination, hexagonal lattices, live-cell imaging, wide-field illumination, different scan modes, etc. These additional features are not even mentioned when compared to the Altair-LSFM. If a comparison is to be provided, it should be fair and balanced. Furthermore, as outlined in the public review, anecdotal statements on "most used", "difficult to align", or "unstable" should not be provided without data.

In the revised manuscript, we have carefully removed anecdotal statements and, where appropriate, replaced them with quantitative or verifiable information. For instance, we now explicitly report that the square lattice was used in 16 of the 20 figure subpanels in the original LLSM publication, and we include a proxy for optical complexity based on the number of optical elements requiring alignment in each system.

We also now clearly distinguish between the original LLSM design—which supports multiple illumination and scanning modes—and its subsequent commercial variants, including the ZEISS Lattice Lightsheet 7, which prioritizes stability and ease of use over configurational flexibility (see Supplementary Note 3).

(6) The authors should recognize that implementing custom optics, no matter how well designed, is a big barrier to cross for most cell biology labs.

We fully understand and now acknowledge in the main text that implementing custom optics can present a significant barrier, particularly for laboratories without prior experience in optical system assembly. However, similar challenges were encountered during the adoption of other open-source microscopy platforms, such as mesoSPIM and OpenSPIM, both of which have nonetheless achieved widespread implementation. Their success has largely been driven by exhaustive documentation, strong community support, and standardized design principles—approaches we have also prioritized in Altair-LSFM. We have therefore made all CAD files, alignment guides, and detailed build documentation publicly available and continue to develop instructional materials and community resources to further reduce the barrier to adoption.

(7) Statements on "hands on workshops" though laudable, may not be appropriate to include in a scientific publication without some documentation on the influence they have had on implanting the microscope.

We understand the concern. Our intention in mentioning hands-on workshops was to convey that the dissemination effort is supported by an NIH Biomedical Technology Development and Dissemination grant, which includes dedicated channels for outreach and community engagement. Nonetheless, we agree that such statements are not appropriate without formal documentation of their impact, and we have therefore removed this text from the revised manuscript.

(8) It is claimed that the microscope is "reliable" in the discussion, but with no proof, long-term stability should be assessed and included.

Our experience with Altair-LSFM has been that it remains well-aligned over time—especially in comparison to other light-sheet systems we worked on throughout the last 11 years—we acknowledge that this assessment is anecdotal. As such, we have omitted this claim from the revised manuscript.

(9) Due to the reliance on anecdotal statements and comparisons without proof to other systems, this paper at times reads like a brochure rather than a scientific publication. The authors should consider editing their manuscript accordingly to focus on the technical and quantifiable aspects of their work.

We agree with the reviewer’s assessment and have revised the manuscript to remove anecdotal comparisons and subjective language. Where possible, we now provide quantitative metrics or verifiable data to support our statements.

Reviewer #3 (Recommendations for the authors):

Other minor points that could improve the manuscript (although some of these points are explained in the huge supplementary manual): 

(1) The authors explain thoroughly their design, and they chose a sample-scanning method. I think that a brief discussion of the advantages and disadvantages of such a method over, for example, a laserscanning system (with fixed sample) in the main text will be highly beneficial for the users.

In the revised manuscript, we now include a brief discussion in the main text outlining the advantages and limitations of a sample-scanning approach relative to a light-sheet–scanning system. Specifically, we note that for thin, adherent specimens, sample scanning minimizes the optical path length through the sample, allowing the use of more tightly focused illumination beams that improve axial resolution. We also include a new supplementary figure illustrating how this configuration reduces the propagation length of the illumination light sheet, thereby enhancing axial resolution.

(2) The authors justify selecting a 0.6 NA illumination objective over alternatives (e.g., Special Optics), but the manuscript would benefit from a more quantitative trade-off analysis (beam waist, working distance, sample compatibility) with other possibilities. Within the objective context, a comparison of the performances of this system with the new and upcoming single-objective light-sheet methods (and the ones based also on optical refocusing, e.g., DAXI) would be very interesting for the goodness of the manuscript.

In the revised manuscript, we now provide a quantitative trade-off analysis of the illumination objectives in Supplementary Note 1, including comparisons of beam waist, working distance, and sample compatibility. This section also presents calculated point spread functions for both the 0.6 NA and 0.67 NA objectives, outlining the performance trade-offs that informed our design choice. In addition, Supplementary Note 3 now includes a broader comparison of Altair-LSFM with other light-sheet modalities, including diSPIM, ASLM, and OPM, to further contextualize the system’s capabilities within the evolving light-sheet microscopy landscape.

(3) The modularity of the system is implied in the context of the manuscript, but not fully explained. The authors should specify more clearly, for example, if cameras could be easily changed, objectives could be easily swapped, light-sheet thickness could be tuned by changing cylindrical lens, how users might adapt the system for different samples (e.g., embryos, cleared tissue, live imaging), .etc, and discuss eventual constraints or compatibility issues to these implementations.

Altair-LSFM was explicitly designed and optimized for imaging live adherent cells, where sample scanning and short light-sheet propagation lengths provide optimal axial resolution (Supplementary Note 3). While the same platform could be used for superficial imaging in embryos, systems implementing multiview illumination and detection schemes are better suited for such specimens. Similarly, cleared tissue imaging typically requires specialized solvent-compatible objectives and approaches such as ASLM that maximize the field of view. We have now added some text to the Design Principles section that explicitly state this.

Altair-LSFM offers varying levels of modularity depending on the user’s level of expertise. For entry-level users, the illumination numerical aperture—and therefore the light-sheet thickness and propagation length—can be readily adjusted by tuning the rectangular aperture conjugate to the back pupil of the illumination objective, as described in the Design Principles section. For mid-level users, alternative configurations of Altair-LSFM, including different detection objectives, stages, filter wheels, or cameras, can be readily implemented (Supplementary Note 1). Importantly, navigate natively supports a broad range of hardware devices, and new components can be easily integrated through its modular interface. For expert users, all Zemax simulations, CAD models, and step-by-step optimization protocols are openly provided, enabling complete re-optimization of the optical design to meet specific experimental requirements.

(4) Resolution measurements before and after deconvolution are central to the performance claim, but the deconvolution method (PetaKit5D) is only briefly mentioned in the main text, it's not referenced, and has to be clarified in more detail, coherently with the precision of the supplementary information. More specifically, PetaKit5D should be referenced in the main text, the details of the deconvolution parameters discussed in the Methods section, and the computational requirements should also be mentioned. 

In the revised manuscript, we now provide a dedicated description of the deconvolution process in the Methods section, including the specific parameters and algorithms used. We have also explicitly referenced PetaKit5D in the main text to ensure proper attribution and clarity. Additionally, we note the computational requirements associated with this analysis in the same section for completeness.

(5)  Image post-processing is not fully explained in the main text. Since the system is sample-scanning based, no word in the main text is spent on deskewing, which is an integral part of the post-processing to obtain a "straight" 3D stack. Since other systems implement such a post-processing algorithm (for example, single-objective architectures), it would be beneficial to have some discussion about this, and also a brief comparison to other systems in the main text in the methods section. 

In the revised manuscript, we now explicitly describe both deskewing (shearing) and deconvolution procedures in the Alignment and Characterization section of the main text and direct readers to the Methods section. We also briefly explain why the data must be sheared to correct for the angled sample-scanning geometry for LLSM and Altair-LSFM, as well as both sample-scanning and laser-scanning-variants of OPMs.

(6) A brief discussion on comparative costs with other systems (LLSM, dispim, etc.) could be helpful for non-imaging expert researchers who could try to implement such an optical architecture in their lab.

Unfortunately, the exact costs of commercial systems such as LLSM or diSPIM are typically not publicly available, as they depend on institutional agreements and vendor-specific quotations. Nonetheless, we now provide approximate cost estimates in Supplementary Note 1 to help readers and prospective users gauge the expected scale of investment relative to other advanced light-sheet microscopy systems.

(7) The "navigate" control software is provided, but a brief discussion on its advantages compared to an already open-access system, such as Micromanager, could be useful for the users.

In the revised manuscript, we now include Supplementary Note 5 that discusses the advantages and disadvantages of different open-source microscope control platforms, including navigate and MicroManager. In brief, navigate was designed to provide turnkey support for multiple light-sheet architectures, with pre-configured acquisition routines optimized for Altair-LSFM, integrated data management with support for multiple file formats (TIFF, HDF5, N5, and Zarr), and full interoperability with OMEcompliant workflows. By contrast, while Micro-Manager offers a broader library of hardware drivers, it typically requires manual configuration and custom scripting for advanced light-sheet imaging workflows.

(8) The cost and parts are well documented, but the time and expertise required are not crystal clear.Adding a simple time estimate (perhaps in the Supplement Section) of assembly/alignment/installation/validation and first imaging will be very beneficial for users. Also, what level of expertise is assumed (prior optics experience, for example) to be needed to install a system like this? This can help non-optics-expert users to better understand what kind of adventure they are putting themselves through.

We thank the reviewer for this helpful suggestion. To address this, we have added Supplementary Table S5, which provides approximate time estimates for assembly, alignment, validation, and first imaging based on the user’s prior experience with optical systems. The table distinguishes between novice (no prior experience), moderate (some experience using but not assembling optical systems), and expert (experienced in building and aligning optical systems) users. This addition is intended to give prospective builders a realistic sense of the time commitment and level of expertise required to assemble and validate AltairLSFM.

Minor things in the main text:

(1) Line 109: The cost is considered "excluding the laser source". But then in the table of costs, you mention L4cc as a "multicolor laser source", for 25 K. Can you explain this better? Are the costs correct with or without the laser source? 

We acknowledge that the statement in line 109 was incorrect—the quoted ~$150k system cost does include the laser source (L4cc, listed at $25k in the cost table). We have corrected this in the revised manuscript.

(2) Line 113: You say "lateral resolution, but then you state a 3D resolution (230 nm x 230 nm x 370 nm). This needs to be fixed.

Thank you, we have corrected this.

(3) Line 138: Is the light-sheet uniformity proven also with a fluorescent dye? This could be beneficial for the main text, showing the performance of the instrument in a fluorescent environment.

The light-sheet profiles shown in the manuscript were acquired using fluorescein to visualize the beam. We have revised the main text and figure legends to clearly state this.

(4) Line 149: This is one of the most important features of the system, defying the usual tradeoff between light-sheet thickness and field of view, with a regular Gaussian beam. I would clarify more specifically how you achieve this because this really is the most powerful takeaway of the paper.

We thank the reviewer for this key observation. The ability of Altair-LSFM to maintain a thin light sheet across a large field of view arises from diffraction effects inherent to high NA illumination. Specifically, diffraction elongates the PSF along the beam’s propagation direction, effectively extending the region over which the light sheet remains sufficiently thin for high-resolution imaging. This phenomenon, which has been the subject of active discussion within the light-sheet microscopy community, allows Altair-LSFM to partially overcome the conventional trade-off between light-sheet thickness and propagation length. We now clarify this point in the main text and provide a more detailed discussion in Supplementary Note 3, which is explicitly referenced in the discussion of the revised manuscript.

(5) Line 171: You talk about repeatable assembly...have you tried many different baseplates? Otherwise, this is a complicated statement, since this is a proof-of-concept paper. 

We thank the reviewer for this comment. We have not yet validated the design across multiple independently assembled baseplates and therefore agree that our previous statement regarding repeatable assembly was premature. To avoid overstating the current level of validation, we have removed this statement from the revised manuscript.

(6) Line 187: same as above. You mention "long-term stability". For how long did you try this? This should be specified in numbers (days, weeks, months, years?) Otherwise, it is a complicated statement to make, since this is a proof-of-concept paper.

We also agree that referencing long-term stability without quantitative backing is inappropriate, and have removed this statement from the revised manuscript.

(7) Line 198: "rapid z-stack acquisition. How rapid? Also, what is the limitation of the galvo-scanning in terms of the imaging speed of the system? This should be noted in the methods section.

In the revised manuscript, we now clarify these points in the Optoelectronic Design section. Specifically, we explicitly note that the resonant galvo used for shadow reduction operates at 4 kHz, ensuring that it is not rate-limiting for any imaging mode. In the same section, we also evaluate the maximum acquisition speeds achievable using navigate and report the theoretical bandwidth of the sample-scanning piezo, which together define the practical limits of volumetric acquisition speed for Altair-LSFM.

(8) Line 234: Peta5Kit is discussed in the additional documentation, but should be referenced here, as well.

We now reference and cite PetaKit5D.

(9) Line 256: "values are on par with LLSM", but no values are provided. Some details should also be provided in the main text.

In the revised manuscript, we now provide the lateral and axial resolution values originally reported for LLSM in the main text to facilitate direct comparison with Altair-LSFM. Additionally, Supplementary Note 3 now includes an expanded discussion on the nuances of resolution measurement and reporting in lightsheet microscopy.

Figures:

(1) Figure 1 could be implemented with Figure 3. They're both discussing the validation of the system (theoretically and with simulations), and they could be together in different panels of the same figure. The experimental light-sheet seems to be shown in a transmission mode. Showing a pattern in a fluorescent dye could also be beneficial for the paper.

In Figure 1, our goal was to guide readers through the design process—illustrating how the detection objective’s NA sets the system’s resolution, which defines the required pixel size for Nyquist sampling and, in turn, the field of view. We then use Figure 1b–c to show how the illumination beam was designed and simulated to achieve that field of view. In contrast, Figure 3 presents the experimental validation of the illumination system. To avoid confusion, we now clarify in the text that the light sheet shown in Figure 3 was visualized in a fluorescein solution and imaged in transmission mode. While we agree that Figures 1 and 3 both serve to validate the system, we prefer to keep them as separate figures to maintain focus within each panel. We believe this organization better supports the narrative structure and allows readers to digest the theoretical and experimental validations independently.

(2) Figure 3: Panels d and e show the same thing. Why would you expect that xz and yz profiles should be different? Is this due to the orientation of the objectives towards the sample?

In Figure 3, we present the PSF from all three orthogonal views, as this provides the most transparent assessment of PSF quality—certain aberration modes can be obscured when only select perspectives are shown. In principle, the XZ and YZ projections should be equivalent in a well-aligned system. However, as seen in the XZ projection, a small degree of coma is present that is not evident in the YZ view. We now explicitly note this observation in the revised figure caption to clarify the difference between these panels.

(3) Figure 4's single boxes lack a scale bar, and some of the Supplementary Figures (e.g. Figure 5) lack detailed axis labels or scale bars. Also, in the detailed documentation, some figures are referred to as Figure 5. Figure 7 or, for example, figure 6. Figure 8, and this makes the cross-references very complicated to follow

In the revised manuscript, we have corrected these issues. All figures and supplementary figures now include appropriate scale bars, axis labels, and consistent formatting. We have also carefully reviewed and standardized all cross-references throughout the main text and supplementary documentation to ensure that figure numbering is accurate and easy to follow.

"noir" vous voulez dire ?

刚发的

这个输液室我测试

我看看如何高亮和测试

这个是我为了测试而已

右面的数组对应左边的树，可以发现parents这个数组是完全冗余的。可以只通过keys这个数组来表示出堆（因为与普通的BST相比，Heap只允许最右下的元素丢失）

Что такое реальность в ПХГ?

Test note

hghgh

100

You can sort of simulate location pricing using other mechanisms?

This implies making them methanol ready is much cheaper than making them hydrogen ready?

Why?

Wow, that is less than I expected across the whole EU

I hadn't considered the political economy here. This is v interesting in the context of aviation

输入关键词搜索这里无法搜到德力西账户，为啥诶？

La enfermedad misma puede cambiar tu personalidadl.

Muy interesante las diferencias en comportamiento según host.

Lo entiendo desde un punto de vista de estrategia, tiene falsos negativos también. Su ataque es generalizable a otras especie? Creería que sí sistemas olfativos parecen lo bastante viejos para ser compartidos

qweqwe

텥스트입니다.

test

HHH BECAUSE FRR ANGOLA WAS A TRUE PAN AFRICAN MARXIST LENINIST STATE

DOI: 10.1126/scitranslmed.adq1965

Resource: (NIH Nonhuman Primate Reagent Resource Cat# PR-1117, RRID:AB_2716330)

Curator: @giovanni.decastro

SciCrunch record: RRID:AB_2716330

What is this?

DOI: 10.1038/s41590-025-02309-1

Resource: (NIH Nonhuman Primate Reagent Resource Cat# PR-0056, RRID:AB_2910535)

Curator: @giovanni.decastro

SciCrunch record: RRID:AB_2910535

What is this?

DOI: 10.1038/s41590-025-02290-9

Resource: (NIH Nonhuman Primate Reagent Resource Cat# PR-0060, RRID:AB_2910539)

Curator: @giovanni.decastro

SciCrunch record: RRID:AB_2910539

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: None

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_GPT:T016059

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (IMSR Cat# JAX_008374,RRID:IMSR_JAX:008374)

Curator: @scibot

SciCrunch record: RRID:IMSR_JAX:008374

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (IMSR Cat# JAX_007900,RRID:IMSR_JAX:007900)

Curator: @scibot

SciCrunch record: RRID:IMSR_JAX:007900

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (IMSR Cat# JAX_028054,RRID:IMSR_JAX:028054)

Curator: @scibot

SciCrunch record: RRID:IMSR_JAX:028054

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Sigma-Aldrich Cat# T7451, RRID:AB_609894)

Curator: @scibot

SciCrunch record: RRID:AB_609894

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (IMSR Cat# JAX_016225,RRID:IMSR_JAX:016225)

Curator: @scibot

SciCrunch record: RRID:IMSR_JAX:016225

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (R and D Systems Cat# AF3628, RRID:AB_2161028)

Curator: @scibot

SciCrunch record: RRID:AB_2161028

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Santa Cruz Biotechnology Cat# sc-25555, RRID:AB_2269914)

Curator: @scibot

SciCrunch record: RRID:AB_2269914

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (R and D Systems Cat# AF3465, RRID:AB_2183550)

Curator: @scibot

SciCrunch record: RRID:AB_2183550

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (DSHB Cat# TROMA-I, RRID:AB_531826)

Curator: @scibot

SciCrunch record: RRID:AB_531826

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: RRID:IMSR_JAX:007909

Curator: @scibot

SciCrunch record: RRID:IMSR_JAX:007909

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Thermo Fisher Scientific Cat# 11-5773-82, RRID:AB_465243)

Curator: @scibot

SciCrunch record: RRID:AB_465243

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Santa Cruz Biotechnology Cat# sc-53665, RRID:AB_629093)

Curator: @scibot

SciCrunch record: RRID:AB_629093

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (R and D Systems Cat# AF1042, RRID:AB_2162633)

Curator: @scibot

SciCrunch record: RRID:AB_2162633

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 905504, RRID:AB_2616956)

Curator: @scibot

SciCrunch record: RRID:AB_2616956

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Thermo Fisher Scientific Cat# 14-5698-80, RRID:AB_10853185)

Curator: @scibot

SciCrunch record: RRID:AB_10853185

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Cell Signaling Technology Cat# 71536, RRID:AB_3101753)

Curator: @scibot

SciCrunch record: RRID:AB_3101753

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 644811, RRID:AB_2287097)

Curator: @scibot

SciCrunch record: RRID:AB_2287097

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Sigma-Aldrich Cat# F3777, RRID:AB_476977)

Curator: @scibot

SciCrunch record: RRID:AB_476977

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (R and D Systems Cat# AF1145, RRID:AB_354628)

Curator: @scibot

SciCrunch record: RRID:AB_354628

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Thermo Fisher Scientific Cat# 14-0081-82, RRID:AB_467087)

Curator: @scibot

SciCrunch record: RRID:AB_467087

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Thermo Fisher Scientific Cat# 14-0194-82, RRID:AB_2637171)

Curator: @scibot

SciCrunch record: RRID:AB_2637171

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (R and D Systems Cat# AF591, RRID:AB_2098565)

Curator: @scibot

SciCrunch record: RRID:AB_2098565

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Santa Cruz Biotechnology Cat# sc-19641 CON, RRID:AB_2009035)

Curator: @scibot

SciCrunch record: RRID:AB_2009035

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Thermo Fisher Scientific Cat# 14-2444-80, RRID:AB_2572867)

Curator: @scibot

SciCrunch record: RRID:AB_2572867

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Santa Cruz Biotechnology Cat# sc-53212, RRID:AB_1120718)

Curator: @scibot

SciCrunch record: RRID:AB_1120718

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (R and D Systems Cat# MAB1179, RRID:AB_2289349)

Curator: @scibot

SciCrunch record: RRID:AB_2289349

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Thermo Fisher Scientific Cat# G-21234, RRID:AB_2536530)

Curator: @scibot

SciCrunch record: RRID:AB_2536530

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Santa Cruz Biotechnology Cat# sc-47778, RRID:AB_626632)

Curator: @scibot

SciCrunch record: RRID:AB_626632

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Thermo Fisher Scientific Cat# G-21040, RRID:AB_2536527)

Curator: @scibot

SciCrunch record: RRID:AB_2536527

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 504102, RRID:AB_315316)

Curator: @scibot

SciCrunch record: RRID:AB_315316

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 102101, RRID:AB_312866)

Curator: @scibot

SciCrunch record: RRID:AB_312866

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Millipore Cat# AB3786, RRID:AB_91588)

Curator: @scibot

SciCrunch record: RRID:AB_91588

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 100506, RRID:AB_312709)

Curator: @scibot

SciCrunch record: RRID:AB_312709

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 102236, RRID:AB_2813928)

Curator: @scibot

SciCrunch record: RRID:AB_2813928

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 104412, RRID:AB_313099)

Curator: @scibot

SciCrunch record: RRID:AB_313099

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_2832274

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 103006, RRID:AB_312957)

Curator: @scibot

SciCrunch record: RRID:AB_312957

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_2129897

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Bio X Cell Cat# BE0058, RRID:AB_1107764)

Curator: @scibot

SciCrunch record: RRID:AB_1107764

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 100302, RRID:AB_312667)

Curator: @scibot

SciCrunch record: RRID:AB_312667

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 102043, RRID:AB_2562611)

Curator: @scibot

SciCrunch record: RRID:AB_2562611

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 123610, RRID:AB_2563544)

Curator: @scibot

SciCrunch record: RRID:AB_2563544

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 100722, RRID:AB_312761)

Curator: @scibot

SciCrunch record: RRID:AB_312761

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 118225, RRID:AB_2563983)

Curator: @scibot

SciCrunch record: RRID:AB_2563983

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 100512, RRID:AB_312715)

Curator: @scibot

SciCrunch record: RRID:AB_312715

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BD Biosciences Cat# 553080, RRID:AB_394610)

Curator: @scibot

SciCrunch record: RRID:AB_394610

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 100322, RRID:AB_389322)

Curator: @scibot

SciCrunch record: RRID:AB_389322

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 505829, RRID:AB_10897937)

Curator: @scibot

SciCrunch record: RRID:AB_10897937

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (BioLegend Cat# 103147, RRID:AB_2564383)

Curator: @scibot

SciCrunch record: RRID:AB_2564383

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Bio X Cell Cat# BE0249, RRID:AB_2687730)

Curator: @scibot

SciCrunch record: RRID:AB_2687730

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Bio X Cell Cat# BE0089, RRID:AB_1107769)

Curator: @scibot

SciCrunch record: RRID:AB_1107769

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Bio X Cell Cat# BE0090, RRID:AB_1107780)

Curator: @scibot

SciCrunch record: RRID:AB_1107780

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Bio X Cell Cat# BE0055, RRID:AB_1107694)

Curator: @scibot

SciCrunch record: RRID:AB_1107694

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Bio X Cell Cat# BE0034, RRID:AB_1107713)

Curator: @scibot

SciCrunch record: RRID:AB_1107713

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Bio X Cell Cat# BE0004-1, RRID:AB_1107671)

Curator: @scibot

SciCrunch record: RRID:AB_1107671

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Bio X Cell Cat# BE0091, RRID:AB_1107773)

Curator: @scibot

SciCrunch record: RRID:AB_1107773

What is this?

DOI: 10.1016/j.stem.2025.12.005

Resource: (Bio X Cell Cat# BE0003-1, RRID:AB_1107636)

Curator: @scibot

SciCrunch record: RRID:AB_1107636

What is this?

DOI: 10.1016/j.redox.2025.103987

Resource: RRID:IMSR_JAX:000664

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:000664

What is this?

DOI: 10.1016/j.nbd.2025.107242

Resource: RRID:BDSC_31391

Curator: @areedewitt04

SciCrunch record: RRID:BDSC_31391

What is this?

DOI: 10.1016/j.nbd.2025.107242

Resource: RRID:BDSC_59604

Curator: @areedewitt04

SciCrunch record: RRID:BDSC_59604

What is this?

DOI: 10.1016/j.nbd.2025.107242

Resource: RRID:BDSC_51358

Curator: @areedewitt04

SciCrunch record: RRID:BDSC_51358

What is this?

DOI: 10.1016/j.nbd.2025.107242

Resource: RRID:BDSC_50632

Curator: @areedewitt04

SciCrunch record: RRID:BDSC_50632

What is this?

DOI: 10.1016/j.nbd.2025.107242

Resource: RRID:BDSC_35600

Curator: @areedewitt04

SciCrunch record: RRID:BDSC_35600

What is this?

DOI: 10.1016/j.canlet.2025.218221

Resource: (CLS Cat# 300266/p487_LOVO, RRID:CVCL_0399)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0399

What is this?

DOI: 10.1016/j.canlet.2025.218221

Resource: (RRID:CVCL_0320)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0320

What is this?

DOI: 10.1016/j.canlet.2025.218221

Resource: (KCB Cat# KCB 200848YJ, RRID:CVCL_0546)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0546

What is this?

DOI: 10.1016/j.canlet.2025.218221

Resource: (RRID:CVCL_0248)

Curator: @areedewitt04

SciCrunch record: RRID:CVCL_0248

What is this?

DOI: 10.1016/j.brs.2025.103012

Resource: (IMSR Cat# JAX_008069,RRID:IMSR_JAX:008069)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:008069

What is this?

DOI: 10.1016/j.brs.2025.103012

Resource: (IMSR Cat# JAX_012569,RRID:IMSR_JAX:012569)

Curator: @areedewitt04

SciCrunch record: RRID:IMSR_JAX:012569

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 301048, RRID:AB_2561942)

Curator: @scibot

SciCrunch record: RRID:AB_2561942

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 305520, RRID:AB_2734277)

Curator: @scibot

SciCrunch record: RRID:AB_2734277

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 300536, RRID:AB_2632791)

Curator: @scibot

SciCrunch record: RRID:AB_2632791

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 318334, RRID:AB_2561912)

Curator: @scibot

SciCrunch record: RRID:AB_2561912

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 300436, RRID:AB_2562124)

Curator: @scibot

SciCrunch record: RRID:AB_2562124

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 302026, RRID:AB_2278418)

Curator: @scibot

SciCrunch record: RRID:AB_2278418

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 351328, RRID:AB_2562908)

Curator: @scibot

SciCrunch record: RRID:AB_2562908

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 339930, RRID:AB_2563968)

Curator: @scibot

SciCrunch record: RRID:AB_2563968

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 306706, RRID:AB_314644)

Curator: @scibot

SciCrunch record: RRID:AB_314644

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 301804, RRID:AB_314186)

Curator: @scibot

SciCrunch record: RRID:AB_314186

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 334608, RRID:AB_1227653)

Curator: @scibot

SciCrunch record: RRID:AB_1227653

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 306014, RRID:AB_2124259)

Curator: @scibot

SciCrunch record: RRID:AB_2124259

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 337214, RRID:AB_2129792)

Curator: @scibot

SciCrunch record: RRID:AB_2129792

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 343504, RRID:AB_1731852)

Curator: @scibot

SciCrunch record: RRID:AB_1731852

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 302226, RRID:AB_493751)

Curator: @scibot

SciCrunch record: RRID:AB_493751

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: (BioLegend Cat# 331208, RRID:AB_1575108)

Curator: @scibot

SciCrunch record: RRID:AB_1575108

What is this?

DOI: 10.1016/j.xpro.2025.104289

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_2860792

What is this?

DOI: 10.1016/j.xpro.2025.104286

Resource: (BioLegend Cat# 300438, RRID:AB_11146991)

Curator: @scibot

SciCrunch record: RRID:AB_11146991

What is this?

DOI: 10.1016/j.xpro.2025.104286

Resource: (BioLegend Cat# 377603, RRID:AB_2910433)

Curator: @scibot

SciCrunch record: RRID:AB_2910433

What is this?

DOI: 10.1016/j.xpro.2025.104283

Resource: (Abcam Cat# ab6728, RRID:AB_955440)

Curator: @scibot

SciCrunch record: RRID:AB_955440

What is this?

DOI: 10.1016/j.xpro.2025.104283

Resource: (Proteintech Cat# 11100-2-AP, RRID:AB_2289959)

Curator: @scibot

SciCrunch record: RRID:AB_2289959

What is this?

DOI: 10.1016/j.xpro.2025.104283

Resource: RRID:Addgene_194607

Curator: @scibot

SciCrunch record: RRID:Addgene_194607

What is this?

DOI: 10.1016/j.xpro.2025.104283

Resource: (Abcam Cat# ab6721, RRID:AB_955447)

Curator: @scibot

SciCrunch record: RRID:AB_955447

What is this?

DOI: 10.1016/j.xpro.2025.104283

Resource: (Proteintech Cat# 10073-1-AP, RRID:AB_2178005)

Curator: @scibot

SciCrunch record: RRID:AB_2178005

What is this?

DOI: 10.1016/j.xpro.2025.104283

Resource: (Sigma-Aldrich Cat# O4886, RRID:AB_260838)

Curator: @scibot

SciCrunch record: RRID:AB_260838

What is this?

DOI: 10.1016/j.xpro.2025.104283

Resource: (MBL International Cat# PM053-7, RRID:AB_10697035)

Curator: @scibot

SciCrunch record: RRID:AB_10697035

What is this?

DOI: 10.1016/j.tet.2025.135113

Resource: Augusta University Chemical and Biomolecular Analysis Core Facility (RRID:SCR_026668)

Curator: @scibot

SciCrunch record: RRID:SCR_026668

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Thermo Fisher Scientific Cat# A-21447, RRID:AB_2535864)

Curator: @scibot

SciCrunch record: RRID:AB_2535864

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Molecular Probes Cat# A-21202, RRID:AB_141607)

Curator: @scibot

SciCrunch record: RRID:AB_141607

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Thermo Fisher Scientific Cat# A-11055, RRID:AB_2534102)

Curator: @scibot

SciCrunch record: RRID:AB_2534102

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Molecular Probes Cat# A-31571, RRID:AB_162542)

Curator: @scibot

SciCrunch record: RRID:AB_162542

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Thermo Fisher Scientific Cat# A-31570, RRID:AB_2536180)

Curator: @scibot

SciCrunch record: RRID:AB_2536180

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Molecular Probes Cat# A-31572, RRID:AB_162543)

Curator: @scibot

SciCrunch record: RRID:AB_162543

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Thermo Fisher Scientific Cat# A32773, RRID:AB_2762848)

Curator: @scibot

SciCrunch record: RRID:AB_2762848

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Thermo Fisher Scientific Cat# A32795, RRID:AB_2762835)

Curator: @scibot

SciCrunch record: RRID:AB_2762835

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Molecular Probes Cat# A-21206, RRID:AB_2535792)

Curator: @scibot

SciCrunch record: RRID:AB_2535792

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Thermo Fisher Scientific Cat# A32766, RRID:AB_2762823)

Curator: @scibot

SciCrunch record: RRID:AB_2762823

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Thermo Fisher Scientific Cat# A32790, RRID:AB_2762833)

Curator: @scibot

SciCrunch record: RRID:AB_2762833

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (R and D Systems Cat# AF1979, RRID:AB_2157172)

Curator: @scibot

SciCrunch record: RRID:AB_2157172

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Thermo Fisher Scientific Cat# A32794, RRID:AB_2762834)

Curator: @scibot

SciCrunch record: RRID:AB_2762834

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Thermo Fisher Scientific Cat# A32849, RRID:AB_2762840)

Curator: @scibot

SciCrunch record: RRID:AB_2762840

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Abcam Cat# ab283260, RRID:AB_2924400)

Curator: @scibot

SciCrunch record: RRID:AB_2924400

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Cell Signaling Technology Cat# 9449, RRID:AB_2797703)

Curator: @scibot

SciCrunch record: RRID:AB_2797703

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_1073663

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Cell Signaling Technology Cat# 48367, RRID:AB_2799337)

Curator: @scibot

SciCrunch record: RRID:AB_2799337

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: None

Curator: @scibot

SciCrunch record: RRID:AB_10697685

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Abcam Cat# ab200040, RRID:AB_2923502)

Curator: @scibot

SciCrunch record: RRID:AB_2923502

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Cell Signaling Technology Cat# 9661, RRID:AB_2341188)

Curator: @scibot

SciCrunch record: RRID:AB_2341188

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Atlas Antibodies Cat# HPA056896, RRID:AB_2683268)

Curator: @scibot

SciCrunch record: RRID:AB_2683268

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Cell Signaling Technology Cat# 14952, RRID:AB_2722487)

Curator: @scibot

SciCrunch record: RRID:AB_2722487

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Santa Cruz Biotechnology Cat# sc-365665, RRID:AB_10846318)

Curator: @scibot

SciCrunch record: RRID:AB_10846318

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Santa Cruz Biotechnology Cat# sc-8002, RRID:AB_627558)

Curator: @scibot

SciCrunch record: RRID:AB_627558

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Abcam Cat# ab108422, RRID:AB_11157157)

Curator: @scibot

SciCrunch record: RRID:AB_11157157

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (R and D Systems Cat# AF1700, RRID:AB_2108901)

Curator: @scibot

SciCrunch record: RRID:AB_2108901

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Cell Signaling Technology Cat# 8757, RRID:AB_2797144)

Curator: @scibot

SciCrunch record: RRID:AB_2797144

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Agilent Cat# M7018, RRID:AB_2134589)

Curator: @scibot

SciCrunch record: RRID:AB_2134589

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Thermo Fisher Scientific Cat# 14-0451-81, RRID:AB_467250)

Curator: @scibot

SciCrunch record: RRID:AB_467250

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (R and D Systems Cat# AF2018, RRID:AB_355110)

Curator: @scibot

SciCrunch record: RRID:AB_355110

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Abcam Cat# ab9582, RRID:AB_296507)

Curator: @scibot

SciCrunch record: RRID:AB_296507

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Cell Signaling Technology Cat# 5741, RRID:AB_10695459)

Curator: @scibot

SciCrunch record: RRID:AB_10695459

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Santa Cruz Biotechnology Cat# sc-12726, RRID:AB_667767)

Curator: @scibot

SciCrunch record: RRID:AB_667767

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (R and D Systems Cat# AF1075, RRID:AB_2077228)

Curator: @scibot

SciCrunch record: RRID:AB_2077228

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Abcam Cat# ab109517, RRID:AB_10866454)

Curator: @scibot

SciCrunch record: RRID:AB_10866454

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Cell Signaling Technology Cat# 81694, RRID:AB_2799983)

Curator: @scibot

SciCrunch record: RRID:AB_2799983

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Cell Signaling Technology Cat# 9115, RRID:AB_2169699)

Curator: @scibot

SciCrunch record: RRID:AB_2169699

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (R and D Systems Cat# AF2605, RRID:AB_2108571)

Curator: @scibot

SciCrunch record: RRID:AB_2108571

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (R and D Systems Cat# AF1924, RRID:AB_355060)

Curator: @scibot

SciCrunch record: RRID:AB_355060

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Santa Cruz Biotechnology Cat# sc-5279, RRID:AB_628051)

Curator: @scibot

SciCrunch record: RRID:AB_628051

What is this?

DOI: 10.1016/j.stem.2025.12.002

Resource: (Abcam Cat# ab128936, RRID:AB_11150990)

Curator: @scibot

SciCrunch record: RRID:AB_11150990

What is this?

DOI: 10.1016/j.scr.2025.103893

Resource: (Thermo Fisher Scientific Cat# A-11001, RRID:AB_2534069)

Curator: @scibot

SciCrunch record: RRID:AB_2534069

What is this?

DOI: 10.1016/j.scr.2025.103893

Resource: (Santa Cruz Biotechnology Cat# sc-21705, RRID:AB_628385)

Curator: @scibot

SciCrunch record: RRID:AB_628385

What is this?

DOI: 10.1016/j.scr.2025.103893

Resource: (Santa Cruz Biotechnology Cat# sc-5279, RRID:AB_628051)

Curator: @scibot

SciCrunch record: RRID:AB_628051

What is this?

DOI: 10.1016/j.scr.2025.103892

Resource: (Miltenyi Biotec Cat# 130-104-993, RRID:AB_2653499)

Curator: @scibot

SciCrunch record: RRID:AB_2653499

What is this?

DOI: 10.1016/j.scr.2025.103892

Resource: (Miltenyi Biotec Cat# 130-111-033, RRID:AB_2653495)

Curator: @scibot

SciCrunch record: RRID:AB_2653495

What is this?

DOI: 10.1016/j.scr.2025.103892

Resource: (Miltenyi Biotec Cat# 130-107-829, RRID:AB_2653168)

Curator: @scibot

SciCrunch record: RRID:AB_2653168

What is this?

DOI: 10.1016/j.scr.2025.103892

Resource: (Miltenyi Biotec Cat# 130-117-370, RRID:AB_2734060)

Curator: @scibot

SciCrunch record: RRID:AB_2734060

What is this?