Oncogenic signaling is coupled to colorectal cancer cell differentiation state

Abstract

Colorectal cancer progression is intrinsically linked to stepwise deregulation of the intestinal differentiation trajectory. In this process, sequential mutations of APC, KRAS, TP53, and SMAD4 enable oncogenic signaling and establish the hallmarks of cancer. Here, we use mass cytometry of isogenic human colon organoids and patient-derived cancer organoids to capture oncogenic signaling, cell phenotypes, and differentiation states in a high-dimensional single-cell map. We define a differentiation axis in all tumor progression states from normal to cancer. Our data show that colorectal cancer driver mutations shape the distribution of cells along the differentiation axis. In this regard, subsequent mutations can have stem cell promoting or restricting effects. Individual nodes of the cancer cell signaling network remain coupled to the differentiation state, regardless of the presence of driver mutations. We use single-cell RNA sequencing to link the (phospho-)protein signaling network to transcriptomic states with biological and clinical relevance. Our work highlights how oncogenes gradually shape signaling and transcriptomes during tumor progression.

Full text

REPORT
Oncogenic signaling is coupled to colorectal cancer
cell differentiation state
Thomas Sell
1,2
, Christian Klotz
3
, Matthias M. Fischer
1,2
, Rosario Astaburuaga-Garc´
ıa
1,2
, Susanne Krug
4
, Jarno Drost
5,6
, Hans Clevers
6,7
,
Christine Sers
1,2,8
, Markus Morkel
1,8,9
, and Nils Blüthgen
1,2,8

Colorectal cancer progression is intrinsically linked to stepwise deregulation of the intestinal differentiation trajectory. In this
process, sequential mutations of APC, KRAS, TP53, and SMAD4 enable oncogenic signaling and establish the hallmarks of cancer.
Here, we use mass cytometry of isogenic human colon organoids and patient-derived cancer organoids to capture oncogenic
signaling, cell phenotypes, and differentiation states in a high-dimensional single-cell map. We define a differentiation axis in all
tumor progression states from normal to cancer. Our data show that colorectal cancer driver mutations shape the distribution of cells
along the differentiation axis. In this regard, subsequent mutations can have stem cell promoting or restricting effects. Individual
nodes of the cancer cell signaling network remain coupled to the differentiation state, regardless of the presence of driver
mutations. We use single-cell RNA sequencing to link the (phospho-)protein signaling network to transcriptomic states with biological
and clinical relevance. Our work highlights how oncogenes gradually shape signaling and transcriptomes during tumor progression.
Introduction
Colorectal cancer (CRC) is one of the most prevalent neoplastic
diseases. It progresses by cumulative acquisition of mutations in
oncogenes and tumor suppressors, each modulating one or more
intracellular signaling cascades (Fearon and Vogelstein, 1990).
Activation of the Wnt/β-Catenin pathway by functional loss of
adenomatous polyposis coli (APC) protein is the initiating event
in a majority of CRCs and promotes a stem cell–like phenotype
(Van de Wetering et al., 2002;Fearon, 2011). Constitutive acti-
vation of the EGFR/MAPK pathway components KRAS and BRAF
occurs in 40 and 5–10% of CRCs, respectively, and promotes
proliferation among other functions (Cancer Genome Atlas
Network, 2012;Rajagopalan et al., 2002). Functional loss of
TP53 occurs in more than 55%of CRCs, disrupts the DNA damage
response, and is closely tied to the adenoma-carcinoma transi-
tion (Baker et al., 1990;Fearon, 2011;Mamlouk et al., 2020). TGF-
βpathway function is impaired by SMAD4 loss, as a late event in
the progression of 10–15% of CRCs (Fearon, 2011). It is not well
investigated how these key drivers shape signal transduction
network activities and differentiation states on the protein level.
Here we analyze a human colon organoid progression series
encompassing driver mutations in APC, KRAS, TP53, and SMAD4
(Drost et al., 2015), using mass cytometry (MC), also known as
Cytometry by time-of-flight (CyTOF). High-dimensional (phos-
pho-)protein data shows specific effects of cell-intrinsic muta-
tions on the signaling network which in turn anchor cells on
different positions on a differentiation axis. Using single-cell
RNA sequencing (scRNA-seq) as an orthogonal technique, we
find that well-defined transcriptome signatures are correlated to
specific cell signaling network states during CRC progression.
Results and discussion
A differentiation trajectory links cell signaling and phenotype
in normal colon organoids
The intestinal epithelium is a complex tissue consisting of a
hierarchy of cell types. We employed MC, a single-cell proteomics
method, to measure cell-type and cell-state markers and simul-
taneously quantify the activity of various intracellular signaling
.............................................................................................................................................................................
1
Institute of Pathology, Charit´
e—Universit¨
atsmedizin Berlin, corporate member of Freie Universit¨
at Berlin and Humboldt-Universit¨
at zu Berlin, Berlin, Germany;
2
Institute
of Biology, Humboldt University of Berlin, Berlin, Germany;
3
Department of Infectious Diseases, Robert Koch-Institute, Unit 16 Mycotic and Parasitic Agents and
Mycobacteria, Berlin, Germany;
4
Department of Gastroenterology, Charit´
e—Universit¨
atsmedizin Berlin, corporate member of Freie Universit¨
at Berlin and Humboldt-
Universit¨
at zu Berlin, Rheumatology and Infectious Diseases, Clinical Physiology/Nutritional Medicine, Berlin, Germany;
5
Princess M´
axima Center for Pediatric Oncology,
Utrecht, Netherlands;
6
Oncode Institute, Utrecht, Netherlands;
7
Hubrecht Institute, Royal Netherlands Academy of Arts and Sciences and University Medical Center
Utrecht, Utrecht, Netherlands;
8
German Cancer Consortium Partner Site Berlin, German Cancer Research Center, Heidelberg, Germany;
9
Berlin Institute of Health at
Charit´
e—Universit¨
atsmedizin Berlin, Bioportal Single Cells, Berlin, Germany.
Correspondence to Nils Blüthgen: nils.bluethgen@charite.de; Markus Morkel: markus.morkel@charite.de
H. Clevers’s current affiliation is Pharma, Research and Early Development of F. Hoffmann-La Roche Ltd., Basel, Switzerland.
© 2023 Sell et al. This article is distributed under the terms of an Attribution–Noncommercial–Share Alike–No Mirror Sites license for the first six months after the
publication date (see http://www.rupress.org/terms/). After six months it is available under a Creative Commons License (Attribution–Noncommercial–Share Alike 4.0
International license, as described at https://creativecommons.org/licenses/by-nc-sa/4.0/).
Rockefeller University Press https://doi.org/10.1083/jcb.202204001 1of14
J. Cell Biol. 2023 Vol. 222 No. 6 e202204001

pathways (Bandura et al., 2009;Ornatsky et al., 2010;Spitzer
and Nolan, 2016). We therefore established a custom MC an-
tibody panel (Fig. 1 A and Table 1). Pilot experiments estab-
lished that the panel is suited for simultaneously assessing
cell states from stemness to differentiation, phenotypes such
as proliferation or apoptosis, and key cell signaling pathway
activity (Fig. 1, B and C).
We assessed cell state heterogeneity in human normal colon
organoids (NCOs), after 4 d of culture in either complete me-
dium (+Wnt) or Wnt-deprived conditions (−Wnt). We prepared
a multiplexed MC sample of two biological replicate experi-
ments (Fig. 2 A and Figs. S1 and S2).
Dimension reduction using uniform manifold approximation
and projection (UMAP) of seven cell-type-specific markers yielded
a clustered representation of cell phenotypes (McInnes et al., 2018;
Fig. 2 B). High signals of intestinal stem cell markers LGR5 and
PTK7 (Barker et al., 2007;Jung et al., 2015) marked a distinct area
from regions high in the differentiation marker Krt20 (Jiao et al.,
2008). EphB2, known to be closely linked to the crypt–villus axis
(Batlle et al., 2002), gradually decreased between the two ex-
tremes. Apoptotic cells, marked by high abundance of cleaved
Caspase 3, formed clusters near the region of differentiated cells.
Wnt withdrawal strongly reduced cells within the stem/crypt
region, as expected (Van de Wetering et al., 2002).
To define a continuous differentiation trajectory within the
dataset, we re-embedded the cell-type marker data into a dif-
fusion map. Unlike UMAPs, which emphasize clustering of high-
dimensional data, diffusion maps preserve transitions between
neighboring points (Coifman and Lafon, 2006). In the space
defined by diffusion components 1 and 2, cells aligned on an
almost one-dimensional path (Fig. 2 C). The phenotypical re-
gions of cells described above in Fig. 2 B were also visible in this
representation, with stem cells clustering at the beginning and
differentiated and apoptotic cells at the end of the inferred
trajectory. As this diffusion map faithfully reproduces the in-
testinal epithelium’s inherent differentiation trajectory, we fit-
ted a principal curve (blue dashed line) to define a pseudo-time
axis (Hastie and Stuetzle, 1989) on which intestinal organoid
cells move along toward differentiation.
We binned the data along the previously defined differenti-
ation axis to assess the distribution of cells along this trajectory
as well as the dynamics of protein markers (Fig. 2 D). In full
medium (+Wnt) conditions, most cells were in the left-sided
bins, representing early, stem-like phenotypes. In contrast, in
(−Wnt) conditions cells shifted toward the right-sided bins that
contained more differentiated cells. Protein marker distribu-
tion along the axis was rather constant, irrespective of culture
conditions: Markers typically associated with stem-like and
Figure 1. A CyTOF panel to analyze cell type, state, and signaling. (A) Visual representation of the antibody panel. Intracellular signaling markers in red,
cell-state markers in yellow, cell-type markers in blue. IKK, IκB kinase; BMPR, BMP receptor; EMT, epithelial-mesenchymal transition. (B and C) Functional
control MC experiments for selected antibodies, using split violin plots with medians. (B) HCT116 cells were subjected to small molecule stimulants and
inhibitors to create positive and negative control conditions. Insulin-like growth factor (IGF; 100 ng/ml, 30 min), EGF (25 ng/ml, 10 min), AZD6244 (MEK
inhibitor; 10 µM, 60 min), TNF-α(20 ng/ml, 30 min.), Neocarzinostatin (500 ng/ml, 30 min). (C) Cell lines with previously known differential expression of total
proteins in question were used.
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Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

proliferating cells such as LGR5, PTK7, PROM1, and Ki-67 were
tied to the left side of the pseudo-differentiation axis, while
differentiation marker Krt20 had its maximum at the two-
thirds waypoint and markers of apoptosis and DNA damage
response, such as cleaved Caspase 3, pChk1/2, and pH2A.X, had
their peak at the very end of the axis.
Multiple intracellular signaling markers were also stronger
coupled to the differentiation state than to the presence or ab-
sence of Wnt (Fig. 2 E). For instance, YAP, p4e-BP1, and pS6
were mostly active in undifferentiated cells, indicating graded
Hippo signaling and high metabolic activity in this region.
However, activity of the MEK/ERK pathway was partly un-
coupled from the differentiation axis, as Wnt withdrawal led to
increased phosphorylation of both markers in undifferentiated
cells. Taken together, the MC analyses show that cell signaling
and cell phenotypes are graded and linked in normal colon
organoids.
Cell-to-cell variance in CRC is controlled by oncogenes and the
differentiation trajectory
To assess interplay between cell phenotypes, signaling, and
oncogenic mutations in CRC progression, we employed a pre-
viously established set of four isogenic human colon organoid
lines derived from NCOs using sequential genetic modifications
(Drost et al., 2015). These lines contain the canonical CRC driver
mutations APC loss, KRAS gain-of-function, TP53 loss, and
SMAD4 loss. The organoids of the progression series are thus
termed A, AK, AKP, AKPS, respectively. We also perturbed
ligand-based signaling by growing all organoids in full medium
containing Wnt and EGF, or medium lacking Wnt and/or EGF for
48 h and subsequently performed MC analysis and scRNA-seq
for orthogonal validation (Fig. 3 A).
Driver mutations affected organoid cell-signal transducers
and transcriptomes. APC, KRAS, and SMAD mutations resulted
in activation of Wnt and MAPK targets and downregulation of
Table 1. Antibody marker panel
Type Target Vendor Clone Host Label
Surface CD133 (PROM1) Miltenyi AC133 ms 154Sm
Surface CD24 Fluidigm ML5 ms 169Tm
Surface CD326 (EpCAM) BioLegend 9C4 ms 167Er
Surface CD44 Fluidigm BJ18 ms 166Er
Surface EphB2 BD 2H9 ms 158Gd
Surface LGR5 Fluidigm 4D11F8 rt 161Dy
Surface PTK7 Miltenyi 188B.12.45 ms 146Nd
Intracellular Cleaved Caspase 3 Fluidigm D3E9 rb 142Nd
Intracellular Cleaved PARP Fluidigm F21-852 ms 143Nd
Intracellular IκBαFluidigm L35A5 ms 164Dy
Intracellular Ki-67 Fluidigm B56 ms 162Dy
Intracellular Krt20 CST D9Z1Z rb 176Yb
Intracellular p-p38 [T180/Y182] Fluidigm D3F9 rb 156Gd
Intracellular p-p53 [S15] CST 16G8 ms 172Yb
Intracellular p4e-BP1 [T37/46] CST 236B4 rb 170Er
Intracellular pAkt [S473] Fluidigm D9E rb 152Sm
Intracellular pChk1 [S345] CST 133D3 rb 148Nd
Intracellular pChk2 [T68] CST C13C1 rb 141Pr
Intracellular pERK1/2 [T202/Y204] Fluidigm D13.14.4E rb 171Yb
Intracellular pH2A.X [S139] Fluidigm JBW301 ms 147Sm
Intracellular pMEK1/2 [S217/221] CST 41G9 rb 151Eu
Intracellular pNF-kB p65 [S536] CST 93H1 rb 155Gd
Intracellular pS6 [S235/236] Fluidigm N7-548 ms 175Lu
Intracellular pSmad1 [S463/S465]/pSmad8 [S465/S467] BD N6-1233 rt 149Sm
Intracellular pSmad2 [S465/467]/pSmad3 [S423/425] CST D27F4 rb 153Eu
Intracellular Vimentin CST D21H3 rb 165Ho
Intracellular YAP CST D8H1X rb 150Nd
All antibodies not purchased from Fluidigm were conjugated in-house. Channels were chosen to minimize potential spillover of high-abundance markers into
low-abundance channels.
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Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

Figure 2. MC reveals differentiation trajectory in NCOs. (A) Experimental workflow. Human NCOs were cultured with orwithout Wnt for 4 d, subsequently
dissociated into single cells, barcoded, pooled, stained with the antibody panel, and measured via MC. (B) UMAP embedding of combined media conditions
based on the seven cell-type markers. Color scale depicts normalized marker signals in the medium condition containing (19,921 cells) or not containing Wnt
(24,501 cells). Stem cell, differentiated, apoptotic regions are marked in red. (C) Diffusion map embedding of combined media conditions based on the cell-
Sell et al. Journal of Cell Biology 4of14
Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

TGF-β/bone morphogenetic protein (BMP) target signatures,
respectively. TP53 and SMAD4 mutations synergized in down-
regulation of the TP53 response (Fig. 3 B). MC could trace back
many of the gene expression changes to alterations in upstream
signals (Fig. 3 C). Most prominently, KRAS mutated organoids
(AK, AKP, APKS) showed increased abundance of phosphory-
lated MEK (pMEK), phosphorylated ERK (pERK), and pS6, but
notably not of pAkt and p4e-BP1. Paradoxically, EGF starvation
led to an increase of pERK although expression of the MAPK
target gene signature was reduced, probably owing to feedback
within the signaling network. Similarly, SMAD4 loss resulted in
increased phosphorylation of related co-factors SMAD1/8, al-
though BMP target transcription was decreased.
Further effects of oncogenic mutations and growth factors
were also correlated between MC and scRNA-seq data (Fig. 3 C):
In NCOs, EGF starvation resulted in a decrease of the MC marker
Krt20 and the enterocyte differentiation transcriptional signa-
ture while DNA damage and apoptosis markers cleaved Caspase
3, pChk1/2, and pH2A.X were increased. The effect of EGF
withdrawal on DNA damage and apoptosis markers was also
seen in the APC mutant line. APC loss led to increased abun-
dances of proliferation, stem cell, and crypt markers while si-
multaneously reducing Krt20, as expected (Van de Wetering
et al., 2002). Wnt withdrawal had no consistent effect on any
progression series organoids, compatible with cell-autonomous
activation of Wnt/β-Catenin signaling after APC loss (Fig. S3, A
and B). Lines harboring further mutations (AK, AKP, AKPS)
were rather inert to phenotypic changes in response to Wnt or
EGF withdrawal. The fully mutated APKS line had highest ac-
tivity of the metabolic integrator 4e-BP1, and also highest pro-
liferative scores in the transcriptome analyses (Fig. 3 C and Fig.
S3 C).
Many of the changes observed in cell signaling and pheno-
types were graded along a differentiation axis. For this analysis,
we divided the dataset into 20 equally sized bins after sorting by
EphB2 abundance (Fig. 3 D; first panel, blue to yellow). In line
with the average EphB2 levels, NCOs predominantly occupied
the bins on the right side, corresponding to more differentiated
cells, while cells of the A line inhabited the left side of the axis.
Subsequent KRAS mutation in AK partly reversed this effect,
while AKPS cells occupied the leftmost bins of the pseudo-
differentiation axis. Unexpectedly, EGF promoted differentia-
tion prior to KRAS mutation but stemness thereafter, based on
the cell distributions along the EphB2 axis. Overall, this analysis
shows that different oncogenic mutations and external growth
factors both shape cell differentiation states and that oncogenic
mutations establish an intrinsically driven cancer signaling
network and cell phenotype in a stepwise manner. These pat-
terns could also be visualized by mean marker abundance
per EphB2 bin (Fig. 3 D,panels2–6, purple to yellow; Fig. S4).
For example, LGR5 and PTK7 are graded along the EphB2
axis, while also varying greatly between cell lines. Krt20 and
cleaved Caspase 3 were graded in the opposite direction,
i.e., higher in EphB2-low cells, and also fluctuated between
mutational and medium conditions. MAPK signal transducers,
in contrast, showed a strong increase after KRAS activation and
further after subsequent TP53 loss, while SMAD4 knockout
boosted AKT signaling.
Based on these observations, we hypothesized that variation
in this dataset could be categorized using three dimensions:
introduced mutations (intrinsic signals), supplied growth fac-
tors (extrinsic signals), and differentiation phenotype. ANOVA
on mean marker abundances showed that markers of stem cell
or crypt progenitor phenotypes LGR5, PTK7, PROM1, CD24,
CD44, YAP were all strongly linked to the EphB2 pseudo-
differentiation axis and were in addition modulated by muta-
tions (Fig. 3 E). In contrast, some signaling molecules such as
pERK1/2, pMEK1/2, and pS6 were mainly explained by the
mutational state, but others, including pAkt, p4e-BP1, and
pSmad1/8, were also modulated along the cell differentiation
trajectory. Only few markers were influenced by the presence
of growth factors, most notably cleaved Caspase 3, which
mainly indicated apoptosis in KRAS wild-type organoids in
conditions lacking EGF. This was reflected by the highest per-
centage of variance explained by the interaction term between
mutational state and growth factors across all markers. Across
most markers, variance explained by replicates was generally
very low, indicating a high reproducibility. Overall, both the
ANOVA and inspection of single markers show that most sig-
naling states in CRC cells are determined by mutation and
differentiation state, and are largely decoupled from external
signals provided by growth factors such as EGF and Wnt.
High-level integration of organoid transcriptome and protein
data
We wanted to correlate main features of the transcriptome to
the MC protein measurements, using the organoid progression
series datasets. In a UMAP, single-cell transcriptomes separated
by driver mutations and external growth factors (Fig. 4 A). We
used non-negative matrix factorization (NMF) to de-composite
the transcriptome data into six modules representing higher
order gene expression information and showing different
strengths across regions of the UMAP (Fig. 4 B). NMF module
1 was related to transcriptomes of NCO cells, whereas the
modules 5 and 6 marked areas within the fully mutated AKPS
line transcriptome clusters. We now quantified activities of
well-characterized and clinically relevant transcriptional sig-
natures in the NMF modules (Fig. 4 C). We considered the Broad
Institute Hallmark signatures (Subramanian et al., 2005), the
Progeny signatures defining transcriptional footprints of sig-
naling pathways (Schubert et al., 2018), and signatures defining
colon and CRC cell heterogeneity (Schwitalla et al., 2013;Mustata
et al., 2013;Cañellas-Socias et al., 2022;Gregorieff et al., 2015;
Wang et al., 2018;Serra et al., 2019;Sato et al., 2010;Barriga
type markers used in B. Fitted principal curve of the diffusion map space is depicted as a dashed blue line. Events downsampled to 10,000 per
medium condition. (D and E) Heatmaps showing cell distributions and mean protein marker signals within equally sized bins along the principal curve
defined in C. Means rescaled per marker and smoothed out using a three-bin-wide running average, 1,000 cell events per bin across conditions.
Sell et al. Journal of Cell Biology 5of14
Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

Figure 3. CRC progression model shows non-continuous path toward stemness. (A) Experimental workflow. Human NCOs, sequentially mutated with
key oncogenes of canonical CRC progression (APC loss of function, KRAS gain of function, TP53 loss of function, SMAD4 loss of function; Drost et al., 2015)
were subjected to three different media conditions and processed into multiplexed scRNA-seq and MC samples. (B) Gene signature scores of scRNA-seq
dataset per line and medium condition. Wnt, MAPK, TP53, and TGF-βby PROGENy (Schubert et al., 2018), BMP by Qi et al. (2017).(C) Log2 fold changes of
mean marker signals relative to complete medium NCO condition. Based on multiplexed MC sample, showing all sequentially mutated lines described in A.Data
in rows sampled to 10,000 cell events per condition. Full circles indicate presence of Wnt or EGF, empty circles omission of Wnt or EGF. (D) MC data binned
along a pseudo-differentiation axis defined by EphB2 marker signal, normalized per medium and line. High EphB2 (stem/transit-amplifying [TA] region) on the
left and low EphB2 (differentiated/apoptotic region) on the right. Data in rows sampled to 10,000 cell events per condition. 7,500 cell events per bin across
Sell et al. Journal of Cell Biology 6of14
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et al., 2017;Smillie et al., 2019;Muñoz et al., 2012;Joanito et al.,
2022;Cortina et al., 2017;´
Alvarez-Varela et al., 2022;Qi et al.,
2017;Ayyaz et al., 2019;Merlos-Su´
arez et al., 2011). As expected
for a module highest in NCO organoids, NMF module 1 scored high
for signatures of normal colon (Joanito et al., 2022), differentiated
cell types (Smillie et al., 2019;Merlos-Su´
arez et al., 2011), and
differentiation-associated BMP signaling (Qi et al., 2017),
among others. NMF modules 2 and 3, related to transcriptomes
of the AK and AKP lines, were defined by strong expression of
oncogenic signaling pathway target genes, most prominently
Wnt, MAPK, P53, and YAP. The non-canonical and KRAS/
BRAF-driven CRC progression signature iCMS3 was distributed
across the NMF 1–3 modules, clustering closely with a EGFR-
driven regenerative cell signature (Gregorieff et al., 2015) and a
signature for high-relapse probability epithelial CRC cells
(Cañellas-Socias et al., 2022). Finally, the NMF 4–6modules,
highest in transcriptomes of cells with the full AKPS set of ca-
nonical drivers, were related to Ki67/E2F proliferative signatures
(Cortina et al., 2017), Myc signaling, the iCMS2 canonical pro-
gression signature (Joanito et al., 2022), and EPHB2 gene ex-
pression. The transcriptome data also could be broken down by
NMF module and signature strength in the single-cell tran-
scriptomes along the progression series (Fig. 4 D), further con-
firming the relationships between oncogenic mutations and
NMF modules, but also highlighting cell-to-cell variation.
Ultimately, we correlated strengths of the RNA-seq–related
NMF modules and the MC protein measurements across the
organoid line and growth factor matrix (Fig. 4 E). We found that
NMF1 was related to apoptosis markers cleaved Caspase 3 and
cleaved PARP, while signal transducers were mostly distributed
across two clusters: One was defined by the correlation of pMEK1/
2, pERK1/2, pS6, and YAP with NMF2–4, whereas the other cluster
was defined by the strong correlation of pSmad1/8, p4E-BP1, and
pAKT with NMF4–6. Taken together, our integrated analysis of
gene expression and protein activity shows that the MC signals are
correlated to the main axes of transcriptome heterogeneity in CRC
progression and well-characterized transcriptome signatures, thus po-
tentially allowing to infer (phospho-)protein network signaling states
from transcriptomes.
Human CRC signaling network activity is also defined by
differentiation state and oncogenes
Unlike our isogenic CRC progression model lines, which inherit
a defined mutational profile, genotypes of individual patient-
derived tumor organoids are complex and heterogeneous. To
investigate if our finding that signaling is largely determined by
oncogenic mutations and cell states extends to human CRC, we
employed 11 well-characterized patient-derived organoid (PDO)
lines (Schütte et al., 2017;Uhlitz et al., 2021). Cell-resolved MC
data of the PDOs showed graded EphB2 signals in most lines.
BRAF-mutant PDOs were an exception as cells had very low
levels of EphB2 and thus clustered at the low end of the pseudo-
differentiation axis. This finding is in line with non-canonical
progressionof BRAF-mutant CRCsthat does not select for LGR5-
positive, EphB2-high stem-like cells.
Many RAS/RAF-wild type or RAS-mutant PDO lines were
somewhat susceptible to changes in growth-factor composition
in the medium, suggesting that they are not fully independent of
Wnt/EGF niche factors, as noted before (´
Alvarez-Varela et al.,
2022). As a general trend, omission of growth factors resulted in
a shift toward higher EphB2 levels in the susceptible lines, ac-
companied by higher average expression of stem-cell marker
genes such as LGR5 (Fig. 5 B). However, growth factor–dependent
and –independent PDO lines scored similarly in ANOVA of sig-
naling network molecules, as activities varied mostly with the
EphB2 axis and not with medium (Fig. 5 C). This analysis implies
that extrinsic growth factors in the medium may determine ranges
of cell differentiation states in a subset of CRC PDO lines, but
signaling network activity within the individual cell is correlated
most strongly with its differentiation state.
Conclusions
Our analysis shows that the cell differentiation state was the
most important determinator of cell signaling and that cancer
drivers restricted permissible cell differentiation states, thus
limiting the impact of extrinsic Wnt and MAPK effectors. Each
of the successive mutations in APC, KRAS, TP53, and SMAD4
had a profound effect on cell differentiation as defined by the
pseudo-differentiation axis. It was somewhat surprising that
cells did not gradually shift toward higher stemness, but were
subject to sometimes opposing effects. While the Wnt effector
APC moved cells toward high expression of stem-cell markers,
mutation of the MAPK signal transducer KRAS promoted dif-
ferentiation. Antagonistic Wnt and MAPK activity has recently
been shown to limit anti-MAPK therapy efficiency in CRC (Zhan
et al., 2019;Uhlitz et al., 2021). Our data extend this antagonistic
model to activities of the APC and KRAS driver mutations during
CRC progression. Additional loss of TGF-βand BMP signal
transducer SMAD4 reversed this KRAS-specific effect in our
data. Loss of SMAD4 could thus contribute to progression of CRC
by promoting cancer cell plasticity toward stemness that was
restricted by prior MAPK activating mutations. Interestingly,
while consecutive mutations also shift mouse CRC organoids
toward stem-cell fate in a recently published MC data set (Qin
et al., 2020;Fig. S5), the effect of KRAS activation on stemness
appears to be different between human and mouse organoid
models. These potential species differences highlight the im-
portance of such studies in human model systems. Collectively,
our results suggest that APC, KRAS, and SMAD4 mutations play
different and partially opposing roles in controlling cell plas-
ticity, which was recently recognized as a key hallmark of
cancer (Hanahan, 2022).
conditions. Leftmost panel color scale is cells per bin, remaining panelsshow mean LGR5, Krt20, cleaved Caspase 3, pERK1/2, and pAkt marker signals per bin.
(E) ANOVA per marker, using mutations (line), supplied growth factors (medium), differentiation (EphB2), an interaction term of mutations and growth factors,
as well as the replicate ID as linear model factors. Color scale shows fraction of variance explained per predictor. Data sampled to 10,000 cell events per
condition.
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Figure 4. NMF analysis integrates single cell transcriptome and protein data. (A) UMAP representation of scRNA-seq dataset. Organoid lines and medium
conditions as previously shown for MC data. (B) NMF modulescores on scRNA-seq UMAP. (C) Correlation of NMF module with gene signature scores related to
colon and CRC cell heterogeneity. (D) Single-cell NMF module scores grouped by organoid line. Color scale is by gene signature scores or EHPB2 expression.
(E) Correlation of NMF module scores with mean MC marker signals across all three replicates.
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Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

Figure 5. Patient-derived CRC organoid signaling networks are largely defined by differentiation state. (A) Average MC marker signals normalized to
marker mean for11 patient-derived CRC organoid lines and 3 medium conditions. Full circles indicate presence of Wnt or EGF, empty circles omission of Wnt or
EGF. Data in rows sampled to 5,000 cell events per condition. (B) Cell distributions per line and medium along a pseudo-differentiation axis defined by EphB2
marker signal across all 11 lines, normalized per medium and line. Data in rows sampled to 5,000 cell events per condition. (C) ANOVA per marker and line,
using medium conditions and differentiation as assessed by EphB2 as linear model factors. Color scale shows fraction of variance explained per predictor.
Values of all ANOVA are shown as heatmap per factor. Data sampled to 5,000 cell events per condition.
Sell et al. Journal of Cell Biology 9of14
Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

We show that cancer drivers limited the impact of extrinsic
Wnt and EGF, both essential factors of the normal colonic tissue
stem cell niche. A subset of patient-derived cell lines was still
susceptible to the growth factors, indicating that CRC signaling
networks are not always fully regulated by oncogenes, as noted
before (´
Alvarez-Varela et al., 2022). Yet, omission of Wnt and/or
EGF in CRC cultures never resulted in collective cell differenti-
ation as seen in the normal colon organoids. Instead, all sus-
ceptible colon lines in our cohort upregulated EphB2 and/or
other stem cell markers as a response to the loss of stem-cell
niche-associated growth factors. This indicates that all CRCs are
hard-wired for the maintenance of a pool of stem-like CRC cells
under a wide range of extrinsic conditions.
Wnt signaling is known to be essential for maintaining in-
testinal stem and crypt cell states, and EphB2 is a known graded
marker of crypt cells (Van de Wetering et al., 2002;Batlle et al.,
2002). Ephrins, including EphB2, are thought to be Wnt targets
and repressors of CRC progression (Batlle et al., 2005). However,
we found that EphB2 expression remained graded also in or-
ganoid lines with engineered loss of the Wnt signal regulator
APC, suggesting that signaling pathways beyond Wnt play a role
in the regulation of EphB2. Interestingly, the Hippo transducer
YAP correlated well with EphB2 expression and could therefore
be a candidate regulator compatible with its role to regulate
stem-cell fate in the colon (Ayyaz et al., 2019;Fig. 2 E). High
EphB2 abundance remained associated with stem-cell markers
and stem-cell-specific cell signaling network states throughout
pre-cancerous and cancerous progressionstates in the canonical
pathway but was generally low in PDOs with BRAF mutations
that probably progressed via non-canonical mechanisms, shed-
ding light on the limitations of EphB2’s ability to predict CRC
relapse in patient cohorts (Merlos-Su´
arez et al., 2011).
We used scRNA-seq to validate the MC (phospho-)protein
analysis. Integration of both data sets allowed us to correlate
high-dimensional transcriptome states to MC signaling network
states via NMF modules. Among further correlations, we found
that transcriptional signatures of revival stem cells (Ayyaz et al.,
2019), EGFR/YAP regenerative cells, (Gregorieff et al., 2015), and
fetal spheroids (Mustata et al., 2013) shared high correlation
with NMF module 3. This module was in the MC signaling data
correlated to high activities of YAP, S6, MEK, and ERK, but low
activities of 4e-BP1 and Smad1/8. Part of these correlations are
backed up by functional analyses, while others predict further,
yet unexplored, features of the respective cell signaling network
states. Thus, integrated analyses of organoids cells encompassing
a greater diversity of organoid models, experimental conditions
and signaling molecules could be used to infer signaling network
states of transcriptomically well-characterized and clinically
relevant cancer cell subpopulations in future experiments.
Our analysis does not shed light on control of DNA replication
and repair, apoptosis, and absorptive lineage allocation in CRC
progression, as fluctuations of markers related to these pro-
cesses, namely pChk2, pH2A.X, cleaved Caspase 3, and Krt20
remained largely unexplained in our analysis of organoids with
well-defined mutations (Fig. 4, B and C). We propose that larger
MC panels and more perturbations are required to uncover how
these processes shape CRC progression.
Materials and methods
Antibody panel
We designed an antibody panel targeting a selection of cell-
type-specific epitopes, multiple cell-state markers, and various
members of intracellular signaling cascades (Table 1 and Fig. 1
A). Most of our antibodies were conjugated in-house using
Maxpar X8 labeling kits, following the manufacturer’s protocol
(201300; Fluidigm).
Antibodies were functionally tested by perturbing HCT116
cells with small molecule substances or by using cell lines with
known differential expression of certain protein markers (Fig. 1,
BandC;Iorio et al., 2016). Signals of antibodies targeting
phosphorylated Akt, S6, and 4-EBP1 (pAkt, pS6, p4-EPB1) were
increased after stimulation of the IGFR/PI3K axis compared to
unstimulated controls (Fig. 1 B). pMEK was increased after
stimulation with EGF and simultaneous inhibition of MEK
function, strengthening EGF-induced ERK to MEK signaling by
eliminating a well-known negative feedback loop (Fritsche-
Guenther et al., 2011). Abundance of pERK was decreased un-
der these conditions due to upstream MEK inhibition. TNF
stimulation led to degradation of IκB and an increased phos-
phorylation of its inhibition target NF-κB(pNF-κB). DNA dam-
age by Neocarzinostatin increased phosphorylation of Chk1,
Chk2, H2AX, and p53. Hippo pathway factor YAP and epithelial
cell marker EpCAM had higher abundances in CRC line HCT116
compared to mesenchymal neuroblastoma line LAN6, as ex-
pected (Fig. 1 C). Epithelial-to-mesenchymal transition marker
Vimentin was mostly absent in HCT116 cells yet strongly ex-
pressed in LAN6. Apoptosis markers cleaved Caspase 3 (Kuida
et al., 1996;Woo et al., 1998)andcleavedPARP(Cohausz and
Althaus, 2009) were higher in CRC organoid line OT326 than
OT227, as expected (Schütte et al., 2017;Brandt et al., 2019). Cell-
type markers LGR5, PROM1, CD24, EphB2, Krt20, and PTK7 all
correlated with previously known RNA expression data of the
cell lines compared (Fig. S6 A;Iorio et al., 2016).
Organoid and cell line culture
The following organoid lines were used in this study: normal
colon organoids, established from human intraoperative mate-
rial (#EA4/015/13; Charit´
e ethics committee approval), isogenic
normal and CRISPR-modified human colon organoids (Drostetal.,
2015), human colon organoid lines OT227, OT302 (Schütte et al.,
2017) and P009T, P013T (Uhlitz et al., 2021).
Organoids were cultured in growth-factor reduced Matrigel
(356230; Corning), according to previously published protocols
(Sato et al., 2009;Sato et al., 2011). Culture medium was Ad-
vanced DMEM/F12 (#12634010; Gibco) supplemented with 1×
GlutaMAX (#35050061; Gibco), 1× N-2 (#17502048; Gibco), 1× B-
27 (#17504044; Gibco), 500 mM N-Acetylcysteine, 1 M Hepes,
100 U/ml Penicillin-Streptomycin, and 100 µg/ml Primocin
(#ant-pm-1; Invitrogen). This base medium was further sup-
plemented with a selection of growth factors, inhibitors, and
conditioned media which varied by line (Table 2). A, AK, AKP,
AKPS media were designed to maintain selection pressure to-
ward the introduced mutations (Drost et al., 2015). R-Spondin
conditioned medium was produced using HEK 293T HA-RSpo-1-FC
clone 3B, according to previously published protocols (Ootani et al.,
Sell et al. Journal of Cell Biology 10 of 14
Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

2009). Wnt was provided as conditioned medium for routine cul-
ture (Wallaschek et al., 2019) or as recombinant protein prior to
measurement (100 ng/ml; Time Bioscience).
For medium perturbation, organoids were re-plated without
disaggregation and kept in base medium supplemented with
combinations of Wnt, R-spondin 1, and EGF for 2 or 4 d before
measurement.
MC
Organoids were collected from culture plates and dissociated
into single cells using a 1:1 mixture of TrypLE Express (12604013;
Gibco) and Accutase (A1110501; Gibco), supplemented with 100
U/ml Benzonase (88700; Pierce). Subsequently, the single-cell
suspension was kept in 5 µM Cisplatin/PBS solution (201064;
Fluidigm) for 5 min at 37°C to stain dead cells and cell debris.
After washing twice with culture medium to remove Cisplatin,
cells were kept for 30 min at 37°C in their original medium
supernatant, which was preserved prior to harvesting the or-
ganoids, to restore culture conditions, including any growth
factors secreted by the organoids. Following this resting phase,
samples were fixed for 10 min at room temperature and sub-
sequently frozen at −80°C. To preserve surface epitopes, we
used proteomic stabilizer PROT1 (NC0627333; Smart Tube, Inc.),
previously shown to be compatible with MC (Gaudillière et al.,
2014). We adapted a recently published protocol (B¨
ottcher et al.,
2019), by mixing PROT1 1:1.4 with 10% PBS/BSA. MC signals of
several key surface and intracellular markers were considerably
higher compared to cells fixed with Maxpar Fix I buffer (201065;
Fluidigm; Fig. S6 B). 1 d prior to measurement, samples were
thawed and barcoded using the Cell-ID 20-Plex Pd Barcoding Kit
(201060; Fluidigm) according to manufacturer’s instructions
and subsequently pooled.
For patient-derived organoid samples, we adapted a previ-
ously described alternative multiplexing protocol (Sufi et al.,
2021). Processing began 2 d prior to measurement with in situ
fixation in 2% formaldehyde (28906; Pierce), followed by an
overnight barcoding step at 4°C using tellurium-based reagents
(Willis et al., 2018). On the following day, organoids were pooled
and jointly dissociated in an enzymatic cocktail of Dispase II
(17105041; Gibco), Collagenase I (17100017; Gibco), Collagenase
IV (17104019; Gibco), DNAse I (A3778; PanReac AppliChem), and
Benzonase (88700; Pierce) combined with mechanical disrup-
tion via a gentleMACS Octo Dissociator (130-096-427; Miltenyi
Biotec) and corresponding C tube (130-093-237; Miltenyi Bio-
tec). Remaining cell clusters were filtered out using a 70 µm cell
strainer.
Multiplexed cell pools were incubated with metal-isotope
tagged antibodies in a two-step protocol. First, antibodies tar-
geting cell-surface proteins (Table 1) were added to the cell
pool for 30 min at room temperature, followed by 10 min
fixation in 2% formaldehyde (28906; Pierce) at room tem-
perature and membrane permeabilization in ice-cold metha-
nol. Subsequently, antibodies targeting intracellular and nuclear
proteins were added to the cell pool for 30 min at room temper-
ature. In between each step, cells were washed in Maxpar Cell
Staining Buffer (201068; Fluidigm). DNA staining was performed
using Cell-ID Intercalator-Ir (201192a; Fluidigm) with a final con-
centration of 62.5 nM in PBS for 20 min at room temperature.
After overnight fixation in 2% formaldehyde (28906; Pierce) at
4°C, stained cells were washed once with Cell Staining Buffer,
twice with doubly distilled water, and filtered through a 30 µm cell
strainer.
EQ Four Element Calibration Beads (201078; Fluidigm) were
added 1:10 to the cell suspension prior to analysis in a Helios
mass cytometer (Fluidigm). The instrument’s software was used
to normalize measured data for machine-related variance based
on the added calibration beads. Further data processing and
plotting was performed in R (R Core Team, 2020) using various
packages of the Tidyverse family (Wickham et al., 2019)and
visually adjusted in Inkscape (Inkscape Project). We used the
CATALYST package for de-convolution of barcoded samples and
spillover compensation (Chevrier et al., 2018). Further pre-
processing steps involved filtering out EQ bead events via the
bead-exclusive 140Ce channel, gating for single cells via event
length and DNA parameters, and excluding dead cells via a
platinum-iridium gate (Fig. S6 C). All MC signals were arsinh
transformed to keep zero values while benefiting of roughly
normal-distributed signal distributions in logarithmic space.
Biological replicates of the CRC progression series experi-
ment were measured in separate MC runs. We computed scaling
factors to match between runs the 85th percentile of each antibody
signal across all perturbation conditions measured, eliminating
Table 2. Custom culture medium conditions for the different lines measured
Substance Source Conc. NCO A AK AKP AKPS PDO
Wnt CM in-house 50% x
Rspo1 CM in-house 5% x
Noggin Peprotech #250-38 100 ng/ml x x x x
EGF Peprotech #315-09 50 ng/ml x x x
Nutlin3 Cayman #10004372 1 µM x x
SB 202190 Sigma-Aldrich #S7067 3 µM x x x x x x
A 83-01 Tocris #2939 500 nM x x x x x x
NCOs were sequentially modified with APC loss (A), KRAS gain-of-function (AK), TP53 loss (AKP), and SMAD4 loss (AKPS). x indicates the presence of the
factor in the custom medium.
Sell et al. Journal of Cell Biology 11 of 14
Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

technical variation while preserving treatment-related effects. We
sampled equal numbers of cells per perturbation condition to en-
sure that the adjustment was not dominated by any specific per-
turbation. Run 2 was adjusted using run 1 as reference, run 3 was
adjusted using the normalized run 2 as reference.
MC data was normalized to account for unwanted covariance
across measured protein markers that may have arisen from
factors such as physical cell size or non-specific staining. For
this, we fitted a multivariate linear model for each channel using
four surrogates of unwanted covariance (namely, Cell-ID palla-
dium barcode, DNA staining, total-ERK, and pan-Akt) and the
dependency was regressed out. This normalization process was
carried out independently for each patient-derived organoid,
while preserving the average intensity of each channel
scRNA-seq
Organoids were collected from culture plates and debris re-
moved through a process of mechanical stimulation and washing
in PBS. Dissociation involved a mixture of TrypLE Express
(12604013; Gibco) and Accutase (A1110501; Gibco) as well as 100
U/ml of DNAse I (PanReac, A3778). The reaction was quenched
in ice-cold culture medium + 0.5% BSA and cells were filtered
through a 40 µm cell strainer. Experimental conditions were
tagged using the antibody-based Human Single-Cell Multi-
plexing Kit (633781; Becton Dickinson) according to the manu-
facturer’s instructions. Subsequently, cells were fixed in an
ice-cold mix of 20% PBS and 80% Methanol for 15 min and
stored at −80°C. For library preparation, equal amounts of cells
per condition were rehydrated in PBS + 1% BSA + 0.5 U/ml
RNAse inhibitor, filtered through a 40 µm cell strainer, and
processed for single-cell sequencing as two pooled samples using
a 10× Chromium Controller and Chromium Next GEM Single
Cell 39v3.1 kits (1000269) according to manufacturer’sin-
structions. Sequencing was performed on an Illumina NovaSeq
6000 system. Raw sequencing data was processed using Cell
Ranger (version 6.1.1, 10× Genomics). Processing of the filtered
count matrices was done in Seurat (version 4.2.0; Hao et al.,
2021) in R (version 4.2.1, R Core Team, 2020). Counts of sam-
ple tags were used to de-multiplex samples by first normalizing
the counts using centered log ratios and subsequently applying
HTODemux. Count data of both libraries was thencombined and
filtered by excluding cells with less than 2,000 detected genes
and more than 20% of reads from mitochondrial transcripts.
Data was normalized using the default parameters of Seurat. The
2,000 most variable transcripts were scaled and used for prin-
cipal component analysis. UMAP representation was calculated
based on the top 10 PCs with default parameters. Signatures
were scored using the AddModuleScore method of Seurat,
PROGENy scores using the progeny library (Schubert et al.,
2018) with default parameters. NMF components were calcu-
lated using the R package NMF (Gaujoux and Seoighe, 2010)
based on the 2,000 most variable genes, after scaling and setting
negatively scaled values to zero. An initial calculation of 3–15
components was used to determine the optimal number of NMF
components using the co-phenetic correlation coefficient as
recommended by the package documentation. Ultimately, six
components were used.
Color scales
Throughout this manuscript, we use various unique color scales
of the viridis and scico packages to depict different analyses
types. CyTOF marker signals are depicted on a black-red-yellow
scale (“inferno”), cell distributions in pseudo-time on a dark blue
to yellow scale (“viridis”), and ANOVA-based plots use a white to
blue scale (“oslo”). All scales use a high contrast range for better
readability and are color-blind friendly.
Online supplemental material
Figs. S1 and S2 show biological replicate data and additional
markers of MC experiments. Figs. S3 and S4 show additional
analyses and alternative visualizations of scRNA-seq and MC
experiments performed with the human CRC progression or-
ganoid series, Fig. S5 shows a re-analysis of mouse organoid MC
data, originally published by Qin et al. (2020),andFig. S6 de-
scribes functional controls for the MC panel, cell processing and
MC data acquisition.
Data availability
All data used in this study and data analysis scripts are available
on Zenodo: https://doi.org/10.5281/zenodo.6400082.
Acknowledgments
The authors thank the Berlin Institute of Health Flow & Mass
Cytometry Core Facility, Gudrun Kliem (Robert Koch-Insti-
tute), and In-Fah M. Lee (Clinical Physiology at Charit´
e) for
their excellent help and technical assistance, Mark Nitz
and Yong Jia Bu (Department of Chemistry at University of
Toronto, Toronto, Canada) for providing TeMal barcoding
reagents.
We acknowledge funding by the German Ministry of Edu-
cation and Research, projects ZiSSTrans (02NUK047E) and
MSTARS (161 L0220A, 16LW0239K), as well as Deutsche For-
schungsgemeinschaft via the graduate programme CompCancer
(RTG2424) and Sachbeihilfe MO 2783/5 (to M. Morkel).
Author contributions: Conceptualization: N. Blüthgen, M.
Morkel, T. Sell; Investigation: T. Sell, C. Klotz, S. Krug; Formal
Analysis and Methodology: T. Sell, M.M. Fischer, R. Asta-
buruaga-Garc´
ıa, N. Blüthgen; Validation: T. Sell; Visualiza-
tion: T. Sell; Resources: J. Drost, H. Clevers, C. Klotz; Funding
Acquisition: N. Blüthgen, M. Morkel; Supervision: N. Blüth-
gen, M. Morkel, C. Sers; Writing—original draft: T. Sell, M.
Morkel; Writing—editing and review: T. Sell, M. Morkel, N.
Blüthgen.
Disclosures: H. Clevers reported a patent to PCT/NL2010/
000017 WO2010/090513 issued “Foundation HUB”;and“Since
March 2022, I am a full-time member of the executive board of
F. Hoffmann-La Roche Ltd. as head of Pharma, Research and
Early Development (pRED) in Basel, Switzerland.”No other
disclosures were reported.
Submitted: 1 April 2022
Revised: 23 December 2022
Accepted: 17 March 2023
Sell et al. Journal of Cell Biology 12 of 14
Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

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Supplemental material
Sell et al. Journal of Cell Biology S1
Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

Figure S1. Biological replicate of Fig. 2.(A)UMAP embedding of combined media conditions based on the seven cell-type markers shown. Color scale
depicts normalized marker signals in ±Wnt conditions. Stem cell, differentiated, apoptotic regions are marked in red. Number of cells: +Wnt = 14,806, −Wnt =
7,632. (B) Diffusion map embedding of combined media conditions based on the cell-type markers also used in A. Fitted principal curve of the diffusion map
space is depicted as a dashed blue line. Events downsampled to 10,000 per medium condition. (C and D) Heatmap showing cell distribution and mean protein
marker signals within equally sized bins along the principal curve defined in B. Means rescaled per marker and smoothed out using a three-bin-wide running
average. 1,000 cell events per bin across conditions. Bins containing less than 30 cells were excluded and are shown in gray.
Sell et al. Journal of Cell Biology S2
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Figure S2. Signals of non-mapping markers for Fig. 2 and Fig. S1.(A)UMAP embedding of replicate 1 data based on seven cell-type markers. Color scale
depicts normalized marker signals in ± Wnt conditions. (B) UMAP embedding of replicate 2 data based on seven cell-type markers. Color scale depicts
normalized marker signals in ± Wnt conditions. Number of cells: +Wnt = 14,806, −Wnt = 7,632.
Sell et al. Journal of Cell Biology S3
Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

Figure S3. Plots of Fig. 3 split by biological replicate. (A) Log2 fold changes of mean marker signals relative to complete medium NCO (replicates1 and 2) or
APCko (replicate 3) condition. Data sampled to 10,000 cell events per condition (row) and replicate. (B) Cell density distributions along a shared pseudo-
differentiation axis defined across replicates by Ephrin B2 marker signal. Normalized per replicate, medium, and line. High Ephrin B2 (stem/TA region) on the
left, low Ephrin B2 (differentiated/apoptotic region) on the right. Data sampled to 10,000 cell events per condition (row) and replicate. 21,000 cell events per
bin across conditions and replicates. (C) Gene signature scores of scRNA-seq dataset per line and medium condition. LGR5 by Merlos-Su´
arez et al. (2011) and
Muñoz et al. (2012), Enterocyte by Smillie et al. (2019), and S phase by Seurat (Tirosh et al., 2016).
Sell et al. Journal of Cell Biology S4
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Figure S4. MC data binned along a pseudo-differentiation axis defined by Ephrin B2 marker signal. Normalized per medium and line. High Ephrin B2
(stem/TA region) on the leftand low Ephrin B2 (differentiated/apoptotic region) on the right. Data sampled to10,000 cellevents per condition (row). 7,500 cell
events per bin across conditions.
Sell et al. Journal of Cell Biology S5
Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

Figure S5. Analysis of Qin et al. (2020) mouse CRC organoid dataset. (A) Cell density distributions along a pseudo-differentiation axis defined by CD44
marker signal. Normalized by line. High CD44 (stem/TA region) on the left and low CD44 (differentiated/apoptotic region) on the right. Number of cells: NCO =
28,590, A = 41,218, AK = 36,005, AKP = 42,405. (B) ANOVA per marker, using introduced oncogene and cell differentiation as described by CD44 signal as linear
model factors. Color scale shows fraction of variance explained per predictor.
Sell et al. Journal of Cell Biology S6
Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001

Figure S6. MC quality controls and gating. (A) Functional control MC experiments for selected cell-state antibodies. Split violin plots with medians. Cell
lines with previously known differential expressions of total proteins in question were used. (B) Comparison of selected marker values after Maxpar Fix I or
Smart tube PROT1 buffer treatment in OT326 CRC organoids. (C) Representative illustration of MC gating strategy for quality assurance.
Sell et al. Journal of Cell Biology S7
Signaling is coupled to CRC differentiation state https://doi.org/10.1083/jcb.202204001