Peer review of: Raman image-activated cell sorting (2022)

Reviewers' Comments: 
 
Reviewer #1: 
Remarks to the Author: 
This manuscript describes the first Raman imaging-activated cell sorting platform based on 
multicolour SRS imaging as wells as applications of which to various cell types. The manuscript is 
well written and the authors should address a few comments as follows: 
 
1. In the introduction, the authors claimed “~600 times richer information content by providing 
Raman images than (non-imaging) activated cell sorting”. Although lots of spontaneous Raman-
activated cell sorting are based on resonance Raman, one should not neglect that one spontaneous 
Raman spectrum (as well as stable-isotope labelled Raman spectrum) contains hundreds of 
intensity information and far richer than a coherent Raman spectrum. 
2. The numbers listed in the introduction should be carefully justified. 
3. The first part of the RESULTS section is lengthy and with lots of technical details. The authors 
should consider putting some of it into METHODS. 
4. In Figure 3 and 4, the chosen wavenumbers of different channels should be labelled on the 
graph. 
In the ABSTRACT and INTRODUCTION, the authors specified the strength of image-activated cell 
sorting in the connection between cell-level analysis and gene-level analysis. However, this 
strength was not illustrated in the experiments. The authors should comment it in the 
DISCUSSION. 
 
 
 
Reviewer #2: 
Remarks to the Author: 
The manuscript by Nitta et al. present a Raman image-activated method for real-time cell sorting. 
This is work is the result of a tremendous amount of effort from a large and diverse team. The 
results are potentially impactful, especially given the label-free chemical specificity that the 
method delivers. I have several comments, mostly minor, as follows. 
1. It would be beneficial to detail better the innovation with respect to the previous work published 
in PNAS 2019. 
2. Can the authors comment on the fundamental limits in terms of throughput, spectral 
multiplexing, etc? What allowed the authors to produce this boost in speed and what keeps them 
from going further? 
3. The main text and supplemental info suggest a rather complex system. Maybe some readers 
might be interested to see an estimate the cost for this instrument compared to a regular flow 
cytometer. 
4. On page 4, the authors use some multipliers to describe performance of their method: “100 
times higher throughput”, “600 times richer information”, “60,000 times more powerful”. To make 
these numbers meaningful, the authors should define quantitatively “throughput”, “powerful”, and 
“rich”. 
5. “Eps” should be defined before its first appearance. 
6. There are some sentences that could use rewarding, e.g., “were straightforward and not very 
complicated”, “wavelength-switched pulse source generates Stokes pulses whose wavelength is 
switched”, etc 
7. This statement in the abstract seems a bit too strong “imaging-based cell picking has 
revolutionized single-cell biology”. Would the authors say that image-based flow cytometry is now 
the norm? Or is it extremely promissing for now? 
In sum, this is an excellent piece of work, with complex imaging, computational, and analysis 
components, demonstrated carefully on various cell types. 
 
 
 
Reviewer #3: 
Remarks to the Author: 

Key results: 
In this manuscript, the authors demonstrated “Raman” image activated cell sorting by integration 
of ultrafast multicolour stimulated Raman Scattering microscopy, digital image processing, and a 
microfluidic cell sorter. The system has been applied to sort various types of cells with different 
treatments, illustrating a maximum throughput of ~100 events per second. 
 
Originality and significance: 
The concept of this work is not new. It is the combination of the previous work from the same 
group, i.e. image-activated cell sorting (Nitta, Cell 2018) and label-free chemical imaging flow 
cytometry by high-speed multicolour stimulated Raman Scattering (Suzuki, PANS 2019). Most of 
the technical platforms (including microfluidic sorter) in this work have been illustrated in their 
previous publications. 
 
As the authors claimed, there is some technical development in terms of imaging acquisition 
scheme (Ling 169) and image construction architecture (line 198). The current implementation 
uses a parallel detection system and constructs images from 24-parallel 4 -colour SRS signals. 
These perhaps have shortened on-line processing time, which is necessary for high throughput 
sorting. Clearly, the group are experts in stimulated Raman Scattering (SRS) and imaging-
activated cell sorting. This work shows the capability of SRS imaging for high throughput cell 
sorting. 
 
However, the title of “Raman image activated cell sorting” is misleading. Generally, Raman often 
refers to spontaneous Raman spectroscopy. However, the work is based on Stimulated Raman 
Scattering. This is not mentioned anywhere in the title and abstract. 
 
The authors seem to overstate the advantage of their approach by saying “our Raman image-
activated cell sorter (RIACS) offers not only ~100 times higher throughput, but also ~600 times 
richer information content by providing Raman images …, making the RIACS ~60,000 times more 
powerful in terms of both population-level and single-cell-level information contents than the 
state-of-the-art technology”. This “60,000 times more powerful..” doesn’t make any sense. Every 
approach has pros and cons. Spontaneous Raman spectroscopy is slow, however, it offers fine 
spectral resolution and is well-established in biological communities. RIACS only uses 4-colour SRS 
imaging, which is essential for its fast spectra acquisition that comes at the expense of spectral 
resolution. In addition, Figures 3, 4 and supplementary figure 9 show the spectral features are 
sufficient to differentiate cells, and the spatial information contained in the images does not seem 
to add significantly to this. 
 
Methodology, statistics and others: 

Although the manuscript is well written, there are still some issues as detailed below: 
1) The authors didn’t clarify where the claimed ~100 events per second comes from. The 
calculation of the evident rates ei in the “sorting throughput” section is confusing. Do the 
histograms of event rates in Figure 2d suggest the event rates vary in a single experiment? 
2) In the “Sorting experiment” section, the authors mention that the diameter of 1um particles 
sample stream is 20 um. This indicates poor focusing for small particles. The authors should 
discuss the limitations of the system for sorting small objectives, since the potential applications 
proposed in the discussion include small sized cells (e.g. synthetic-cell sorting and bacterial-cell 
sorting). 
3) If the authors sort particles one by one, they should provide evidence. 
4) For the cell sorting experiments, no information is given about statistics. It is not clear whether 
the purity and yield data are average values or from a single experiment. 
5) In figure 2b, the scale bar is missing.