Second, p21+ senescent cells remain a rare population even in old tissues, limiting the number of cells available for training and potentially reducing classifier power. This highlights the need for high-speed Raman microscopy, larger multi-tissue datasets, and integration of consensus senescence signatures.
Good call out!
Extended Data Fig.9
Minor point- I'm finding this difficult to read, especially the tables.
hyperspectral Raman imaging (600-1800 cm⁻¹, 873 dimensions with a pixel size of 3 µm)
How did you account for spatial mixing that may occur with the given analyzed spot size? It's possible that a neighboring cell signal could be contributing to the target cell.
we manually selected corresponding cellular keypoints across both imaging modalities. This selection tool then generated a 3×3 transformation matrix to adjust the STARmap images to align with the Raman regions. The manual alignment process utilized a least-squares method, employing a modified two-dimensional version of Horn’s (1987) algorithm to account for differences in translation, scale, rotation, and reflection. For each Raman-STARmap paired sample, hundreds of keypoints were manually selected, and the fitgeotrans function in MATLAB was used to transform the STARmap image to match the Raman region. The imshowpair function was employed iteratively after every 20 keypoints to ensure satisfactory alignment.
I appreciate that this is an important and sometimes tricky problem, but well worth doing! Did you consider using an accuracy metric for the registration?
In mouse skin, elevated Raman peak clusters at 1112-1141 cm⁻¹ (e.g., 1128 cm⁻¹, myristic acid; 1130 cm⁻¹, 12-methyltetradecanoic acid (15Aiso); 1131 cm⁻¹, palmitic acid and other fatty acids; 1134 cm⁻¹, 13-methylmyristic acid (15iso); 1135 cm⁻¹, 15-methylpalmitic acid (17iso)) and at 1434-1444 cm⁻¹ (e.g., 1438 cm⁻¹, palmitic acid; 1439 cm⁻¹, vaccenic acid; 1440 cm⁻¹, oleic acid), coinciding with transcriptomic downregulation of genes involved in ECM (Postn) and muscle contraction (Tnnt3, Ttn)86,87 and upregulation of keratinization genes (Sbsn, Lor)88 (Fig. 5d). In contrast, decreased peaks at 933-948 cm⁻¹ (e.g., 934 cm⁻¹, D-(+)-mannose; 940 cm⁻¹, amylopectin) and 1161-1165 cm⁻¹ (e.g., 1161 cm⁻¹, quinoid ring deformation; 1162 cm⁻¹, adenine) were associated with downregulation of DNA damage response gene Eepd1, and upregulation of skin barrier-maintenance genes (Sfn, Krt10).
Similar comment here regarding overinterpretation of the Raman peaks. Unless you use another complementary technique that is truly capable of molecular structure/identity characterization, this is an overstatement. However, I think your overall point still stands regarding lipid-associated C-C modes as relevant signatures and markers.
In mouse lung, we identified distinct Raman peak clusters, including 602-630 cm⁻¹ (607 cm⁻¹, glycerol; 614 cm⁻¹, cholesterol ester), 1106-1111 cm⁻¹ (1108 cm⁻¹, α-D-glucose; 1109 cm⁻¹, amylopectin), and 1128-1135 cm⁻¹ (1130 cm⁻¹, 12-methyl-tetradecanoic acid (15Aiso); 1131 cm⁻¹, palmitic acid; 1134 cm⁻¹, 13-methylmyristic acid (15iso); 1135 cm⁻¹, and 15-methylpalmitic acid (17iso)).
Could you explain how you are justifying the molecular assignments here? I understand these peaks can be tied to specific vibrations (e.g. 1120-1140 cm-1 can be C-C stretching) which molecules such as lipids may have, but using this as the basis for identifying a specific molecule seems to be an overinterpretation. Other fatty acids, for example, may have very similar peaks, within the error of the instrument especially.
Specifically, we focused on the fingerprint region of Raman spectra (600-1800 cm-1, 930 of the 1,340 features in a Raman spectrum)
Though you already report high accuracy with your method, do you think the result would improve or change if you also included the CH stretch region (~2700-2900 cm-1 or so)?
The exposure time for each point in the Raman measurement was 20 msec, and laser power at the sample plane was 212 mW.
Thanks for sharing this work! Did you notice any alteration in Raman signal of cells due to laser exposure? How did you select the acquisition parameters?
Whether this has a direct relationship with eukaryogenesis remains unknown.
Do you have plans to test this hypothesis?
Indeed, we found that membranes made of stiff bolalipid molecules can exhibit stiffness that is more than an order of magnitude larger than that of bilayer lipids at the same membrane fluidity.
That's an impressive range!
As a result of this distinct lipid composition and geometric organisations, archaeal membranes tend to have markedly different properties from eukaryotic and bacterial membranes. This feature of their membrane biochemistry is thought to be responsible, in part, for the ability of some archaea to survive under conditions like those found in volcanic springs at temperatures > 75oC, despite them lacking a cell wall.
Interesting! Do archaeal membranes generally have this structure, or is it mainly for extremophilic archaea?
Asgard
Have you considered doing a similar analysis centered on another archaeal kingdom, for instance, Methanobacteriati (which presumably also share some key associations, like isoprenoid biosynthesis pathways) that aren't a top candidate for the template for eukaryotes? This could be a test of the basic premise but possibly also reveal other connections to archaea.
For some biological functions, this diverse bacterial component accounted for the majority of the aELW, and roughly one third of the analyzed KOGs (918 of 3045), and EPOCs (2640 of 6990) were associated neither with known ancestors of endosymbionts, Alphaproteobacteria and Cyanobacteria, nor with Asgard. However, in a sharp contrast to Archaeal associations including information processing, protein glycosylation and trafficking, and others, or oxidative phosphorylation and sulfur metabolism for Alphaproteobacteria, EPOCs associated with diverse other bacteria showed few if any coherent functional trends.
Do you have a hypothesis for why certain bacterial clades were more closely associated than others?
These pangenomes include only those genes that are present in at least 67% of the families within each class of bacteria and archaea (see Methods) resulting in an initial database of 13 million sequences
Thanks for a very interesting paper! How did you decide on this cutoff threshold?
The results showed that 75 out of the 80 encrypted peptides exhibited antimicrobial activity (MIC ≤64 μmol L−1) against at least one pathogenic strain (Fig. 3a), resulting in a hit rate of over 93%.
Did you observe any similarities between the peptides that did not show antimicrobial activity, for instance archaeasin-8, 16, and 43?
After this measurement, samples underwent a 20-minute acclimation in darkness.
How was this duration for acclimation chosen? I've seen similar durations in other papers, but I've always been curious why.
MuSpAn is an extensive platform which enables multiscale analysis of spatial data, ranging from the subcellular to the tissue-scale.
Very cool package! Later on in the text, you say "In future work, MuSpAn will be extended to accommodate higher dimensional datasets, e.g., three dimensional spatial data, and longitudinal data which describes dynamic changes in tissue organisation." - do you have plans for how you would extend the platform, particularly for dynamic changes that may be captured on video?
SRS and CARS Microscopy
Was there any concern that the scanning would cause damage to the samples (e.g. through heating or photodamage), and how were these concerns mitigated?
SRS images are acquired sequentially at a range of wavenumbers, as in the representative images shown in
What parameters (power, dwell time, scan duration) were used for this acquisition?
Typical animal cell lines like CHO cells would die if the volume expansion strain rate caused by the water influx exceeds 0.5%/sec.56
I'm curious how you chose CHO cells as a benchmark?
Functions of cellular ultrafast motility
Did you also find commonalities in the aqueous environments that ultrafast single-cell organisms tend to live in?
Therefore, we argue in favor of a balance between top-down and bottom-up approaches: whiledefinitely needing to open the species box, at the same time must we focus, to identify the driversof macroevolution, on microscopic models that remain parsimonious.
I think in general, this makes a lot of sense. Do you foresee any difficulties in going for this type of "happy medium" approach? Would it be more difficult to create models that are useful, or would more resources be required, for instance? I know the expected trade-offs are mentioned later, but I'm more curious about any barriers to putting archetypal models into practice.
Overall, μG has a negative impact on viral replication, whilst BBR has a positive one and the306combination of these stresses has an even greater negative impact than by themselves.
This is a really interesting result! Could you elaborate on the types of further studies that could be undertaken to understand this effect better?
fluorescence generation, and heat production
Have you considered the role of carotenoid fluorescence in these destinations? Would that be considered within NPQ?
our work revealed how frigid environments can impose constraints on microbes and the eukaryotic cell-cycle. The design principles that govern yeast’s life at frigid temperatures and our systems-level approach that uncovered these principles may serve as a case study for future investigations that aim to find similar design principles for other microbes and microbial communities in frigid environments.
This is a really important study in my opinion, because it targets a very big question with a well designed set of experiments. How far do you think you can extrapolate to understand the survival of other organisms based on this model?
LC-MS/MS metabolomics
This makes a lot of sense, but out of curiosity, are there any classes of secondary metabolites that would not be easily detected with this method?
Aspergilli encode an extensive number of secondary metabolite (SM) backbone genes, with notable differences across sections.
In the figure, there are three terpene categories listed, two of which appear to be possible subsets of another. What is the difference between, for instance, 'Terpene' and 'other terpene'? Thanks!
The ultra-multiplex CARS microscope has been described in detail in a previous study [29].
How was the instrument calibrated prior to use? Were polystyrene beads the main standard sample used, as described in 29?
In this study, we observed binuclear senescent cells with CARS microscopy and found the peak of the nucleolar amide I band shifted to a higher wavenumber in the spectra.
Did you also notice or evaluate shifts in other peaks (e.g. Amide III)? Why or why not? Reference 45 mentions Amide III, for instance.
Also, what was the threshold to determine if a shift was real, especially in comparison to the expected error (cm-1) of the instrument?
CARS
More details about determining what constitutes a significant shift, particularly in comparison to the expected error of the instrument, would be welcome!
We focused our analysis on two protein secondary structures, the α-helix and the β-sheet. The reason is that the spectral changes were caused mainly by the formation of unfolded/misfolded proteins and their aggregation. Amyloid-like aggregates induce extensive conformational changes from α-helices to β-sheets during their formation. Images were created for the ratios 𝑔2, 𝕩/(𝑔1, 𝕩 + 𝑔2, 𝕩).
Out of curiosity, would the higher wavenumber shift be due to the secondary structure creating a more constrained conformation, and smaller bond length? Not sure if I have the right logic but am curious!
These are: (1) the peak wavenumbers were higher than those of the control cells, and (2) these peak wavenumbers were also higher than those in the cytoplasm of the same cell (typically at 1653 cm-1). In detail, the peak shifts in the nucleoplasm were not distinct compared to those in the nucleoli.
How much higher were they (cm-1)?
The vibrational mode of water (1640 cm-1) had already been removed.
Did removing this mode increase the noise of this region of the spectrum (if by subtraction)?
However, the molecular basis of this technique is not well understood.
I find this sentence to be a bit vague. What do you mean by "molecular basis"? While the setup and hardware for the types of Raman are different, in all cases, molecular vibrations are interrogated.