Peer review of: Context specificity of the EMT transcriptional response (2020)

Reviewers' comments: 
 
Reviewer #1 (Remarks to the Author): Expertise in ATAC-seq and EMT 
 
This article entitled « Context specificity of the EMT transcriptional response” is proposed by David 
P. Cook & Barbara C. Vanderhyden and is interested in the identification of robust/universal 
transcriptional signature of EMT. Several articles already reported EMT-related genes lists and 
others demonstrated that no doubt there is not a single EMT program but several EMT, and cells 
which are more or less engaged in mesenchymal phenotypes. However, the method used in this 
manuscript is interesting in the way that the authors systematically analyzed transcriptomes using 
scRNA-seq in 4 different cell models +/- 3 EMT inducers in a kinetic. They also analyzed the effect 
of +/- 16 kinase inhibitors in these transcriptional programs. To do that, they generated about 104 
000 transcriptomes. Although this attractive strategy and impressive number of data, this article 
lacks a clear message and is too preliminary for be considered in Nature communications. 
 
Specific Comments: 
 
The choice of the different cell lines and EMT-inducers is not clear. A549 cells are frequently used 
in the literature for studying EMT (since these cells are not fully epithelial cells), but what about 
the others? Moreover, the paper is constructed on the analysis of the transcriptomes obtained 
following the exposition to 3 different EMT-inducers. Not clear that all 3 inducers could really 
induce EMT in the 4 different cell lines. Some specific cell lines are wells described to respond to a 
specific inducer. Could we consider all the transcriptomes without scoring the EMT status? Pictures 
presented in Supp Fig1 suggest that many conditions are not associated with EMT (MCF7 with 
TGF/TNF/EGF, DU145 with EGF, OVCA420 with EGF/TNF). Authors could detect modifications of 
transcriptomes following treatment but not clear here whether these modifications are obviously 
related to EMT. Western-blot with E-CADH/N-CADH/VIM status, length of cell measurement… may 
help to determine which transcriptomes have to be considered. Idem, profiles of UMAP embedding 
for EGF conditions look like very different. What does it means (no EMT ?...) 
 
Authors chose to take genes that were differentially expressed in at least 8 of their conditions. This 
cut-off is not explained. The paper is lacking a solid message. The authors proposed a list of 86 
genes that they also found in variable cancer cells “as well as healthy epithelium from various 
mouse tissues”. This last sentence is not clear? Not convincing if the genes are found in cancer 
models and healthy tissues. What is the specificity of these genes? Are these genes 
associated/correlated with clinical parameters in patients/models (progression free survival, 
metastasis, migration…)? 
May these genes a common regulation (what about transcription factor binding prediction in the 
promoters? common epigenetic regulation?) 
 
All along the manuscript, it is difficult to understand what is experimentally done without be 
obliged to constantly refer to Mat & section or tables. (few examples: “EMT time course”, “several 
EMT genes, “variety of effects”, “no EMT controls”…) Some details should be added in the main 
text. Why authors used these 22 inhibitors (does it refers to the 16 molecules written above in the 
manuscript ?) is not evident to understand. 
 
The name of the target of the molecule under brackets is confusing. Ex: “LY364947 (TGFBR1)” 
should be replaced by “LY364947 (TGFBR1i)” 
 
The authors showed that inhibition of EGF may partially block induction via TGF. This should be 
better explained. Could the MAPK pathway be involved in this process since MAPK is in a crosstalk 
between the non smad pathway (TGFBR) and the EGFR pathway. Molecular experiments are 
needed to support this observation. 
 
Kinase inhibitors are used concomitantly with EMT inducers. It could be useful to repeat some 

experiments with a pre-treatment (e.g. one day before) with the inhibitors to analyze whether 
inhibition of the target kinases could really block EMT. 
 
The specific choice of the OVCA420 cells for the ATAC-seq experiment is not explained 
 
Some papers previously reporting data also observed in the present paper should be cited. 
Gemmil RM et al 2011 Cancer Lett showing that cancer cells are not epithelial or mesenchymal but 
frequently between these phenotypes. 
Peixoto P et al 2019 Cell Death Dis, showing with transcriptomes in different cell models that gene 
expression was more closely dependent from the cell line than the EMT induction. 
Du L et al Cancer Med, analyzing the signature of TGFb-induced EMT 
Cieslik M et al Epigenetics Chromatin showing the links between epigenetics and transcriptomes in 
EMT 
 
 
 
Reviewer #2 (Remarks to the Author): Expertise in single-cell seq and EMT 
 
Cook et al. offer a comprehensive single cell transcriptomic analysis of EMT that explores a variety 
of cancer cell lines exposed to different EMT inducers with a time series experiment that also 
includes a drug inhibition study. Data is generated through multiplexed single cell RNA sequencing. 
The resulting dataset is rich and will likely serve as a landmark dataset that will be further 
explored by many others for years to come. 
 
Using the unique data they generated, the authors make several conclusions about EMT. Notably, 
they find that EMT is a context-dependent process, with transcriptional dependency on the cell line 
and EMT-inducing factors tested. Four different cell types were probed (A549, lung; DU145, 
prostate; MCF7, breast; OVCA420, ovarian) and each was separately treated by one of three 
different EMT-inducing factors (TGFB1, EGF, and TNF). The authors demonstrate that many of the 
commonly cited EMT Hallmark genes are not differentially expressed after treatment. The authors 
produce a new EMT gene list that they claim is most invariant to the conditions tested. Their drug 
screening analysis on the EMT-induced cells with small molecule inhibitors further demonstrates 
context-dependent effects. 
 
Overall, the study addresses timely and elusive issues in the EMT/cancer field and provides a 
valuable new dataset as a resource that can be further mined by the broader community. While 
the authors present the experiments clearly, the take home points derived from the data analytics 
are overly broad and not unexpected. Indeed, it is already recognized that EMT is a context-
dependent and heterogeneous process. The data generated here may be able to quantify the EMT 
process in an unprecedented manner but the analysis provided is largely descriptive and leaves 
several important questions unanswered. Listed below are such questions, together with 
comments that point to potential limitations of the study. 
 
1) Given the vast number of EMT studies, including recent single-cell analysis studies 
(scRNAseq specifically), why do the authors not mention those studies and put their work in that 
current landscape? Some important and highly relevant studies include Pastushenko et al 
“Identification of the tumour transition states occurring during EMT”, van Dijk et al: “Recovering 
gene interactions from single-cell data using data diffusion.” There are many others. 
 
2) Given that the data is generated with single cell resolution, there is surprising little to no 
analysis of cell clusters that may be similar or unique to the various conditions tested. For 
example, some relevant cell clusters may be the partial EMT states previously reported in the 
literature. The data that the authors have generated offer the opportunity to explore this 
possibility and frame the results of this study into a more meaningful context for the broader field. 
 

3) The authors claim that the EMT hallmark gene set contains many genes that are not 
differentially expressed by any of cancer cell lines treated by various EMT-inducing factors. This 
finding is not surprising as it has been documented that some of the genes in the EMT Hallmarks 
are derived from stromal cells (i.e. fibroblasts) in the tumor microenvironment that coordinate the 
mesenchymal transformation of the epithelial malignant cells through paracrine signaling. These 
stromal cell types are captured in bulk tumor gene expression studies. Many deconvolution efforts 
are demonstrating the heterogeneity of cell types contributing to bulk-derived gene expression 
signatures. 
 
4) The authors provide a new EMT gene list that they claim is most invariant to the conditions 
tested and more suitable for assessing EMT, despite its context-dependence. However no 
discussion is provided on the new genes being proposed and why they may be biologically relevant 
to EMT. This is a limitation of the paper, given that the gene list is a main result of the paper. In 
addition, it could be instructive to examine if specific genes from their list are altered during the 
kinase inhibitor treatments. Such a finding could explain the effects of the inhibitors in the context 
of the authors’ main findings. 
 
5) The definition of EMT across the different conditions seems to be “whatever happens after 7 
days under the influence of the EMT inducer” regardless of the cell line and the treatment 
condition. This limitation is not addressed. From the pictures provided in Extended Data Figure 1, 
EMT-related morphological changes are not as evident in some cell lines under certain conditions. 
Do all the cell lines express the receptors of the given ligands? Can references be provided as to 
whether all these ligands induce EMT in these cell lines? For the MET process, it is not clear that 
under all conditions all cells reverted after 3 days as stated for Figure 2b. Was there a reason a 
longer withdrawal time-course was not tested? 
 
6) Are there any data showing that the direct targets of all inhibitors used in Figure 4C are present 
in these cell lines? Protein levels would be preferable even though the paper focused on RNA. 
Although all effects are assessed relative to EMT “output”, this would shed light on whether some 
of the results are off target effects. Also, given that the results of Figure 4 are assessed as an EMT 
dependence on paracrine signaling, it would be helpful to annotate which paracrine signaling is 
inhibited with each inhibitor. For example, PI3K kinase is involved in many different signaling 
pathways (not only paracrine), and has been implicated with many of the molecules stated to be 
inhibited by the drugs tested. 
 
7) Regarding the discussion around the results from RIPK1inhibition, how are EMT and TGFb 
related to necroptosis? What does blocking cells “half way into EMT” mean? Are the authors 
referring to a partial EMT state? Are the effects of RIPKI (and some of the other inhibitors) 
confirmed by EMT-related morphological assessment? Do these drugs inhibit TGFb-induced EMT 
morphological changes? Are any of these results agreeable with some protein data? EMT can 
manifest at the level of posttranslational modifications of common markers such as E cadherin, 
(e.g. Aiello et al. 2018). 
 
 
 
Reviewer #3 (Remarks to the Author): Expertise in EMT and drug screening 
 
The manuscript submitted by Cook and Vanderhyden reports that epithelial-mesenchymal 
transition (EMT) involves various transcript expression profiles among four carcinoma cell lines 
exposed to three distinct EMT inducers. 
The study is based on single-cell sequencing at different time-points and in the presence of kinase 
inhibitors. ATAC-seq was performed in one cell line. Cells from each well were barcoded to allow 
demultiplexing pooled cells from each of the 96-well plate experimental designs. 
Bioinformatics approaches were applied for quality controls and for establishing the most variable 
gene list across the different time-points of the EMT kinetics. A pseudo-temporal ordering is 

provided for each cell line during EMT progression, spontaneous reversal and after reversal by 
selected drugs interfering with the EMT inducer signaling pathways. The ATAC data obtained with 
the OVCA420 carcinoma cell line showed good concordance with the transcription profiles. 
 
Comments 
It is not clear why, although the authors consider 12 time-points for the growth kinetics, only 7 
time-points are described, including 3 time-points for spontaneous reversal. 
Only slightly more than half of the 103,999 cells were analyzed. 
TNF is not usually used as EMT inducer. What motivated the authors to use this cytokine and 
which TNF have they used? 
The authors have selected 4 cell lines with very distinct phenotypes and degree of responses to 
each EMT inducer. 
Extended Figure 1a shows that A549 lung adenocarcinoma cells exhibit mixed EMT phenotypes, as 
shown by multiple studies. This cell line is already engaged in the EMT process at baseline; it 
responds well to the three inducers used in this study, with no obvious cell death. In contrast 
MCF7—one of the most epithelial-like cell lines of breast carcinoma—is known to be quite 
refractory to EMT inducers. Extended Figure 1a suggests that very few cells succeed in engaging 
into EMT only with TNF. A similar comment can be made for the two other cell lines: DU145 is a 
rather mesenchymal-like cell line that, again, responds differently to the three inducers with quite 
distinct morphologies. OVCA420 is epithelial-like cell line that exhibits very distinct responses to 
the three inducers. Again, one can suspect that TGFb1 induces cell death. The cell counts shown in 
extended Figure1c reflect this issue. 
In Figure1d, EGF treatment led to very modest shifts for all 4 cell lines. 
The EMT hallmark (Figure1f) shows a similar profile for all conditions. How can this be explained 
with these very distinct morphologies? 
The EMT score used in this study (Figure 2d) is not significantly increased and, in several cases, 
does not vary in the pseudo-time analysis. 
The EMT signature derived from the four cell lines was then used to analyze two databases for lung 
cancer and one database for normal mouse tissues (Extended Figure 4). The graphs do not reveal 
any significantly better results than other signatures. For instance, it well known that basal cells 
have EMT features, and that most tumors exhibit at a partial EMT phenotype (see for instance Tan 
et al. EMBO Mol Med, 2014). 
The authors used 22 small-molecular compounds with variable specificity to induce EMT reversal in 
the 4 cell lines in the absence and presence of the EMT inducers. Some of these drugs, as 
expected, dampened the response to the inducers. Not surprisingly, TGFbetaR inhibitor affected 
EGFR signaling in A549 cells, since TGFbetaR requires receptor tyrosine kinase signaling for 
activation in a number of cell lines (Derynck and Budi Sci Signal 2019). 
The authors conclude that the expression profiles vary considerably in each condition and that only 
a subset of well-known EMT genes are revealed in this study. This finding is not surprising due to 
the choice of these 4 cell lines of different origins with distinct EMT features at baseline and 
variable response to the inducers. Notably, several EMT expression profiling signatures do not 
include most of the canonical EMT genes, which are often not highly expressed albeit detected by 
PCR. 
Studies of EMT in development show very distinct signaling systems in gastrulation and in 
subsequent EMT events in morphogenesis and organogenesis. In cancer, it is also apparent that 
different tumor types will utilize different signaling pathways to gain intermediate EMT stages. 
There is also increased evidence for intratumoral heterogeneity for the EMT spectrum in individual 
cancer cells. The authors should consider these concepts in their Discussion.