Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line

ARTICLE

Multi-omic single-cell snapshots reveal multiple

independent trajectories to drug tolerance in a

melanoma cell line

Yapeng Su

1,2,3,15

, Melissa E. Ko

4,15

, Hanjun Cheng

, Ronghui Zhu

, Min Xue

1,14

, Jessica Wang

Jihoon W. Lee

, Luke Frankiw

, Alexander Xu

, Stephanie Wong

, Lidia Robert

, Kaitlyn Takata

Dan Yuan

, Yue Lu

, Sui Huang

, Antoni Ribas

5,6,7,8

, Raphael Levine

6,8,9

, Garry P. Nolan

, Wei Wei

3,6,8

Sylvia K. Plevritis

, Guideng Li

12,13

✉

, David Baltimore

✉

& James R. Heath

1,3,6,8

✉

The determination of individual cell trajectories through a high-dimensional cell-state space is

an outstanding challenge for understanding biological changes ranging from cellular differ-

entiation to epigenetic responses of diseased cells upon drugging. We integrate experiments

and theory to determine the trajectories that single BRAF

V600E

mutant melanoma cancer

cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can

yield snapshots of the cell-state landscape, the determination of individual cell trajectories

through that space can be confounded by stochastic cell-state switching. We assayed for a

panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug

treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-

tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a

lineage-restricted transcription factor. Each trajectory exhibits unique druggable suscept-

ibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell

population.

https://doi.org/10.1038/s41467-020-15956-9

OPEN

Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, USA.

Division of Biology and Biological Engineering,

California Institute of Technology, Pasadena, California, USA.

Institute for Systems Biology, Seattle, Washington, USA.

Cancer Biology Program, Stanford

University School of Medicine, Stanford, California, USA.

Department of Medicine, University of California, Los Angeles, Los Angeles, California, USA.

Department of Molecular and Medical Pharmacology, UCLA, Los Angeles, California, USA.

Department of Surgery, UCLA, Los Angeles, California, USA.

Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, California, USA.

The Fritz Haber Research Center, The Hebrew University, Jerusalem, Israel.

Department of Microbiology and Immunology, Stanford University, Stanford, California, USA.

Department of Radiology, Stanford University, Stanford,

California, USA.

Center of Systems Medicine, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College,

Beijing, China.

Suzhou Institute of Systems Medicine, Suzhou, China.

Present address: Department of Chemistry, University of California, Riverside,

Riverside, California, USA.

These authors contributed equally: Yapeng Su, Melissa E. Ko.

✉

email:

lgd@ism.cams.cn

;

baltimo@caltech.edu

;

jheath@systemsbiology.org

NATURE COMMUNICATIONS

| (2020) 11:2345 | https://doi.org/10.1038/s41467-020-15956-9 | www.nature.com/naturecommunications

1

1234567890():,;

C

ellular processes ranging from the development of drug-

tolerant states in cancer cells to stem cell differentiation

can be described as cell-state changes. Speci

fi

cally, certain

cancer cells that are initially responsive to targeted inhibitors that

act against these oncogenic drivers

can evolve into a drug-

tolerant state via non-genetic mechanisms, perhaps preceding the

emergence of drug-resistant clones

–

. The molecular details of

how the cancer cells transition between the two states can inform

the use of additional drugs designed to arrest the transition

–

Previous studies have uncovered mechanistic insights of drug

tolerance at the signaling, metabolic, transcriptional, and epige-

netic levels

. However, most of these studies either compared

drug-tolerant cells and drug-sensitive cells only at bulk level

without single-cell resolution or did not provide a detailed time-

resolved characterization of the trajectories connecting the two

states. We hypothesize that there could be multiple independent

paths accessible to the cells between the drug-sensitive and drug-

tolerant states. If this is true, then the challenge of

fi

nding drug

combinations that can arrest the unfavorable cell-state transition

is signi

fi

cantly increased. Here we investigate a highly plastic

cancer cell line that, when treated with a targeted inhibitor,

switches from a rapidly dividing drug-responsive state to a drug-

tolerant, slow-cycling state within a few days. We show that

the cells can indeed take multiple classes of trajectories between

the two states. Each trajectory class is characterized by a

unique signaling and metabolic network with distinct drug

susceptibilities.

From a functional perspective, cell-state changes are often

accompanied by changes in gene expression

–

, protein

signaling

–

, and cellular metabolism

–

. Highly mul-

tiplexed single-cell methods

–

can provide powerful tools for

mapping out cell-state landscapes associated with cell-state

changes

–

. However, capturing the trajectories that indivi-

dual cells take as they traverse those landscapes is challenging,

even for the case of an isogenic cell line. This is because multiplex

single-cell omics methods only provide snapshots of the occupied

cell-state space at a given instant. Measured similarities between

cells captured at successive time points can imply probable paths

through the landscape

–

. However, cells may stochastically

switch from one state to another, so an individual cell may not

take a smooth trajectory between states. Time-lapse imaging

methods can map individual cell trajectories, but for only two to

three analytes for each cell, and so provide a limited view of the

cell-state space

–

. Thus, the ability to extract cellular trajec-

tories from a kinetic series of cell-state space snapshots would

have a high value. Here we report on combined experimental and

theoretical approaches towards addressing this fundamental

challenge.

We utilize a patient-derived

BRAF

V600E

mutant melanoma

cancer cell line

as a model for the rapid development of drug

tolerance against targeted inhibitors. Under BRAF inhibition,

these highly plastic cells rapidly transit from a drug-responsive

state to a drug-tolerant state

. We characterize this transition

using integrated single-cell functional proteomic and metabolic

assays designed to broadly sample proteins and metabolites

associated with selected cancer hallmarks and cell-state-speci

fi

processes. Dimensional reduction, information-theoretic analysis,

and visualization of the time-series single-cell data uncovers a

complex cell-state space landscape and hints at the possibility of

two distinct paths between drug-naive and drug-tolerant states.

Further experiments test whether these paths constituted inde-

pendent cellular trajectories. In fact, we

fi

nd that even isogenic

tumor cells can undertake different, independent trajectories to

drug tolerance. The two trajectories are associated with distinct

signaling and metabolic networks, and are independently drug-

gable. This

fi

nding challenges the current paradigm of targeted

inhibitor resistance development and also provides guidelines for

assessing the value of combination therapies.

Results

Single-cell proteomic and metabolic analysis of BRAFi adap-

tation

. We characterized drug adaptation in individual melanoma

cells by assaying for a panel of selected proteins, plus glucose

uptake, in BRAF

V600E

mutant M397 cell cultures during the

fi

rst

5 days of BRAFi treatment using the Single Cell Barcode Chip

(SCBC)

–

(Fig.

a). Following 0, 1, 3, and 5 days (D0

control, D1, D3, and D5) of drug treatment, individual cells were

isolated into nanoliter-volume microchambers within an SCBC.

Each isolated cell was lysed in situ to release its cellular contents.

Each microchamber within an SCBC contains a full barcode array

in which each barcode element is either an antibody for speci

fi

protein capture

or a molecular probe designed to assay for a

speci

fi

c metabolite via a competition assay

(Fig.

a). The

design of this panel was informed by transcriptomic analysis of

BRAFi-treated M397 cells (Supplementary Fig. 1) and existing

literature

. The panel broadly samples various func-

tional and metabolic hallmarks of cancer and cell-state markers.

Single-cell pro

fi

ling of BRAFi-naive (D0) M397 cells revealed

heterogeneous levels of many assayed markers at baseline.

Referring to Fig.

b, c and Supplementary Fig. 2, certain analytes

exhibited high variability across the cell population. These

include the melanocytic lineage transcription factor MITF and its

downstream melanocytic cell-state marker MART1, the meta-

bolic regulators HIF1

and p-AMPK

, and the proliferation

marker Ki67. The variance in Ki67 implies that the population

contains both rapid-cycling and slow-cycling cells. By contrast,

high glucose uptake and the expression of metabolic enzymes

lactate dehydrogenase (LDH) and PKM2 were relatively uniform

from cell-to-cell. Drug treatment initially (at D1) inhibits glucose

uptake and represses most metabolic regulators and signaling

phosphoproteins, as well as Ki67. The repression of these cancer

hallmarks re

fl

ects blockage of the key oncogenic signaling

pathway upon initial BRAF inhibition. The drug also promotes

transient cell differentiation followed by dedifferentiation, as

evidenced by an increase of MART1 expression in D3 followed

by its downregulation in D5. However, a small subpopulation of

M397 cells remained Ki67-High in D1, implying a slower drug

response in that subset of cells. At D3, most analytes exhibit a

sharp and transitory increase in variance, which shrinks by D5.

This change includes all of the metabolic regulators except

p-LKB, all resistant state markers and regulators except Slug, all

of the metabolic enzymes, and all of the signaling phosphopro-

teins. The increased magnitude of the

fl

uctuations of many

markers at D3, based upon previous reports

, implies one or

more cell state changes near this time point. This was also

con

fi

rmed by

fl

ow cytometry analysis (Supplementary Fig. 3). By

D5, glucose uptake increased back to near D0 levels, but with

increased variance. Ki67 is further decreased and with a sharply

decreased variance relative to D0. In fact, most cells in D5 enter a

state of senescence, without an increased incidence of apoptotic

cell death (Supplementary Figs. 4 and 5). In addition, at D5, the

variance and abundance of the epithelial-mesenchymal transi-

tion-related transcription factor, Slug, has increased, indicating

the emergence of some cells that are trending towards a

mesenchymal phenotype. Further, the levels of the other assayed

protein markers that are associated with drug resistance (AXL,

N-cadherin, NGFR, and TNFR) were all higher by D5. The

changes of these markers were also con

fi

rmed via

fl

ow cytometry

analysis (Supplementary Fig. 6). The upregulation of glucose

uptake and many resistance markers indicates that cells have

initiated drug resistance programs by D5. Thus, single-cell

ARTICLE

NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-15956-9

2

NATURE COMMUNICATIONS

| (2020) 11:2345 | https://doi.org/10.1038/s41467-020-15956-9 | www.nature.com/naturecommunications

integrated proteomic and metabolic analysis, when viewed at the

level of individual analytes, provides evidence of initial drug

response at D1, a drug-induced cell-state change at D3, and

emerging drug tolerance at D5, prior to an increase in cell

proliferation (full drug resistance), which has been shown to

occur a few weeks later. These observations are all consistent

with the existing literature

Dimensional reduction analysis implies multiple trajectories

Simultaneous visualization of the time-dependent, coordinated

changes across multiple markers requires algorithms that can

reduce the high-dimensionality of the dataset. We applied three

such algorithms: the FLOW-MAP

, t-SNE

, and PHATE

. All

approaches provided an intuitive representation of the dataset

(Fig.

and Supplementary Figs. 7

–

9). FLOW-MAP analysis

revealed that melanoma cells clustered primarily based upon drug

exposure time (Fig.

, upper left plot), indicating chronological

cell-state trajectories. Most untreated M397 cells (in the lower left

of the graph) were characterized by uniform levels of all measured

analytes excepting N-cadherin, MITF, HIF1

, Ki67, and MART1

(see analyte-speci

fi

c plots of Fig.

and Supplementary Fig. 7).

Most of these non-uniformly expressed proteins exhibit differ-

ences that vary gradually from left-to-right across the D0 cluster

of cells, with a small subpopulation of untreated cells (right side

of D0 cluster) exhibiting lower expression of Ki67, MITF, and

MART1. These features point to a small group of dedifferentiated,

slow-cycling cells. Upon BRAFi treatment, the cell populations

initially split to occupy two regions of the FLOW-MAP. At D1

(green points), the majority of the cells cluster to the upper right

of the D0 cells, whereas a small subpopulation clusters directly to

the right of the D0 group. This trend continues at D3, with most

cells clustering above the largest D1 mass, while a small number

cluster to the right of the small D1 group. By D5 (purple), all cells

cluster to the right-hand side of the graph. The bifurcation of cells

at Day 1 and 3 implies the possibility of upper and lower tra-

jectories towards the drug-tolerant state. The possibility of two

classes of trajectories was also indicated by t-SNE

and PHATE

analyses (Supplementary Figs. 8, 9). Thus, computational analyses

Day 0

Day 1

Day 3

Day 5

Drug sensitive

cell state

Drug-tolerant

cell state

BRAFi

Metabolite and Metabolic Enzyme

Sensitive State Marker/Regulator

Metabolic Regulator

Proliferation

Marker

Signaling Transducer

Resistant State Marker/Regulator

Integrated single-cell proteomic & metabolic profiling

Glucose

PFK

PKM2

LDH

p-ACAC

p-LKB

PDK1

HIF1

p-AMPK

NGFR

Slug

TNFR

N-cadherin

AXL

MITF

MART1

p-ERK1

p-NF

B-p65

p-SRC

Ki67

Day

0.20

0.15

0.10

0.05

0.00

–0.05

–0.10

–0.15

–0.20

Glucose

9.5

9.0

8.5

8.0

7.5

p-AMPK

7.5

7.0

6.5

6.0

5.5

p-NF

B-p65

7.5

7.0

6.5

6.0

5.5

5.0

NGFR

Day 0

Day 1

Day 3

Day 5

Day 0

Day 1

Day 3

Day 5

Day 0

Day 1

Day 3

Day 5

Day 0

Day 1

Day 3

Day 5

7.5

7.0

6.5

6.0

5.5

5.0

Valve

Single-cell chamber

Lysis buffer

Lysed cell

Metabolite detection

(Nano-probes based)

Protein detection

(Antibody based)

Fig. 1 Single-cell proteomic and metabolic analysis of early drug response in M397 cells. a

The single-cell integrated proteomic and metabolic analysis

experiments design. Cells from different time points during BRAFi treatment are collected and individually analyzed using the micro

uidic-based single-cell

barcode (SCBC) technology. Each cell was characterized for the levels of six different categories of markers.

Heatmap representation of integrated

proteomic and metabolic analysis dataset. Each row represents an individual cell and each column (except the last column) represents an individual

analyte, with the color in the heatmap representing the measured level of the analyte. The last column represents the number of days after starting BRA

treatment. On the X-axis, markers are colored corresponding to which of the six functional categories they belong to.

Violin plot representation of the

distribution of certain representative markers across four time points. Y-axis represents the natural log of the measured marker level. Each plot is

bordered

by the color of the functional category of the measured marker.

NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-15956-9

ARTICLE

NATURE COMMUNICATIONS

| (2020) 11:2345 | https://doi.org/10.1038/s41467-020-15956-9 | www.nature.com/naturecommunications

3