Peer review of: Multiplexed single-cell transcriptional response profiling to define cancer vulnerabilities and (2019)

Reviewers' Comments: 
 
Reviewer #1: 
Remarks to the Author: 
The is a very mature and clearly written manuscript. The authors present an approach for SNP 
based demultiplexing of single cell RNA-seq data to measure transcriptional effects of 
perturbations. This is a very active research field and the contribution is very timely. The methods 
proposed and used are all very solid and state-of-the art. 
 
I really like the didactic way of presenting the linear model in Fig2, which really explains what is 
being modelled here. 
 
I only have two minor comments: 
- for consistency, would it hurt to decide on either tSNE or UMAP for visualisation, rather than 
switching from one to the other between figures? 
- in the methods section, in the multiplexing model, the authors use a logistic regression model - 
this is a supervised model, so on top of the X_ij you also need a label for each SNP i (=alternative 
allele or not). I did not see a clear description on how these labels (= the ground truth) were 
assigned and ideally would like to see this clarified. 
 
Florian Markowetz 
 
 
 
Reviewer #2: 
Remarks to the Author: 
McFarland et al developed a method (MIX-Seq) to measure post-perturbational transcriptomic 
response at single-cell resolution multiplexed across a wide range of cell lines by resolving the 
identity of each cell based on single nucleotide polymorphism (SNP) profiles from mRNA. Utilizing 
this method, they profiled transcriptomic response of 13 drugs at multiple time points across a 
range of cancer cell lines characterized previously in DepMap. Based on these profiles, the authors 
first identified drug-specific and shared transcriptional response signatures that: 1. Are informative 
about the drug’s mechanism of action (MOA), via benchmarking on the subset with known MOAs, 
2. Are associated or are independent of sensitivity, 3. could predict long-term viability response, 
and finally, 4. Could predict heterogeneity of the response. Finally, combining this approach with 
cell-hashing they performed dual-multiplexing across time points or dosages. 
 
Overall, this is a well-written manuscript describing the experimental and statistical methods 
clearly and briefly. 
 
Comments: 
 
1. An additional validation for resolving the identity of each cell based on SNP profiles from mRNA 
could be performed by integrating pre/post- treatment transcriptomic profiles from CMAP 
(Subramanian et al 2017, for ~20K small molecules for 1000 genes across 9 cancer cell lines from 
DepMap across two time points and multiple dosages) for the subset drugs and cell lines used in 
this study that are common in CMAP. In some more detail, we propose: (for every drug 
separately) Each cell transcriptomic response from MIX-seq can be compared based on the 
similarity (by a correlation test) to CMAP-based nine cell lines transcriptomic response for the 
matched drug. Identification of cell lines (out of nine) can be done based on this correlation rho 
which can act as labels to compare against SNP-based method predictions. Importantly this 
framework could be used to integrate other CMAP like datasets like DRUG-seq (Chaoyang Ye et al 
2018) and PLATE-seq (Bush, E. C. et 2017). 
 
2. It would also help to compare the long-term viability prediction power of treatment response 

transcriptome profile from single-cell seq based MIX-seq vs bulk-seq based from CMAP. 
 
3. The last section highlights a later-onset regulation of viability-related genes vs viability-
independent genes. We would like to point out that viability-related genes could have a later onset 
due to the identification methodology rather of underlying biology. Viability-related genes are 
differentially expressed in sensitive vs non-sensitive cell lines. This is because when the sensitive 
cells subset is targeted it leads to a population decrease with treatment time at a higher extent 
than non-sensitive cell lines. Thus, the extent of differential expression of viability-related would 
increase with time. Hence, need to be corrected for during this analysis.