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CellComm infers cellular crosstalk that drives haematopoietic stem and progenitor cell development

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  • Harvard Medical School, Boston Children's Hospital
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Abstract and Figures

Intercellular communication orchestrates a multitude of physiologic and pathologic conditions. Algorithms to infer cell–cell communication and predict downstream signalling and regulatory networks are needed to illuminate mechanisms of stem cell differentiation and tissue development. Here, to fill this gap, we developed and applied CellComm to investigate how the aorta–gonad–mesonephros microenvironment dictates haematopoietic stem and progenitor cell emergence. We identified key microenvironmental signals and transcriptional networks that regulate haematopoietic development, including Stat3, Nr0b2, Ybx1 and App, and confirmed their roles using zebrafish, mouse and human models. Notably, CellComm revealed extensive crosstalk among signalling pathways and convergence on common transcriptional regulators, indicating a resilient developmental programme that ensures dynamic adaptation to changes in the embryonic environment. Our work provides an algorithm and data resource for the scientific community. Lummertz da Rocha et al. present CellComm, an algorithm that analyses cell–cell communication to predict signalling and regulatory networks, and identify regulators of haematopoietic development in the aorta–gonad–mesonephros region.
Experimental validation of ligands and receptors a, Whole-mount in situ hybridization (WISH) for runx1/cmyb in control and itgb3b morpholino-injected embryos at 36 hpf. Scale bar, 100 µm, N = 23 embryos (control) and N = 26 embryos (itgb3b morpholino). b, Representative flow plots for tested surface receptors on hemogenic endothelial cells from hiPSCs (HE, CD34⁺CD45⁻, day 8) and Hematopoietic cells (CD34⁺CD45⁺, day 8 + 7), pre gated on live, single cells. c, qPCR confirming significant reduction of CD93 transcript 48 h after siRNA transfection. Bars represent mean + /- SD. N = 4 independent EHT cultures. Paired t-test, **p = 0.0065. d, Flow cytometric analysis of CD93 surface expression 48 h after siRNA transduction. Bars represent mean + /- SD. N = 3 independent differentiation experiments. Paired t-test, one-tailed, *p = 0.0261. e, CFU assay from d7 floating cells after hiPSC-derived EHT with and without CD93 siRNA KD. Bars represent mean + /- SD of N = 6 assays. f, WISH timecourse for zebrafish mfap2 across the window of EHT. Scale bar 500 µm. g, Maximum intensity projection of a confocal z-stack of double fluorescent in situs for zebrafish mfap2 and runx1 mRNA at 24 hpf. Vasculature is visualized with GFP antibody staining in transgenic embryos. Yellow arrowhead indicates position of cross section. Scale bars 50 µm, 25 µm and 10 µm. h, Viability and quantification of floating, hematopoietic cells at d8 + 7 of endothelial to hemogenic transition at increasing concentrations of recombinant human MFAP2. Mean + /- SD is plotted, N = 6 independent differentiation experiments. i, Representative flow plots (left) and violin plots (median and quartiles) for CD34 and CD45 staining of floating hematopoietic cells after hiPSC derived EHT culture in the presence of increasing recombinant human MFAP2 concentrations (N = 6 independent experiments). RM One-way ANOVA, p-values have been adjusted for multiple comparison using Dunnett’s test, **p = 0.0029. Source numerical data are available in source data. Source data
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Further prioritization of transcriptional regulators a, The endothelial-to-hematopoietic transition trajectory reconstructed by CellRouter using sorted ECs and T1preHSCs. b, Transcriptional regulators predicted to be important for the cell state transitions reported in the x-axis, for example EC.HE means the differentiation trajectory from EC to HE. c, Gene expression distribution of genes selected for experimental validation. The middle line in the box plot indicates the median. The lower and upper hinges correspond to the 25th and 75th percentiles. Whiskers show min to max. Data beyond the whiskers are ‘outlying’ points and are plotted individually. d, Kinetic profiles of selected genes along the CellRouter trajectory from endothelial cells (ECs) to the T1preHSC10 state. e, Kinetic profiles of Gfi1b and Gfi1 along the EC to HE differentiation trajectory. f, Expression dynamics by qPCR of hematopoietic transcripts RUNX1C and GFI1 as well as CellComm predicted candidate regulators APP and YBX1. N = 4 (d1), N = 6 (d4), N = 5 (d7) independent EHT cultures, N = 2 independent CB CD34 + donor samples. Bar graphs represent mean. g, Kinetic profiles of selected genes along the CellRouter predicted EHT trajectory using the dataset generated by¹. h, Increase in APP and YBX1 expression upon ITGB3 inhibition in hiPSC-derived HSPCs. N = 6, from 3 independent cultures. Bar graphs represent mean + /- SD. i, Cell cycle signature scores along the CellRouter trajectory from EC to the T1preHSC10 state. j, Clustered kinetic profiles, which group genes with similar gene expression trends along the reported CellRouter trajectory. k, Gene ontology enrichment of gene sets in each kinetic cluster identified in l. The color and size scales both represent the -log10 (P value) of the pathway enrichment result. Hypergeometric test. The p-values were adjusted for multiple comparisons using the Benjamini-Hochberg method. Source numerical data are available in source data. Source data
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Experimental validation of transcriptional regulators a, Stat3 inhibitor dose-response curve on runx1/cmyb expression in zebrafish embryos treated from 14-36hpf. b, Representative flow plots for CD34 and CD45 staining of floating hematopoietic cells after hiPSC derived EHT culture in the presence of increasing STAT3 inhibitor concentrations. Bar graphs represent mean + /- SD. Ordinary One-way ANOVA, Dunnett’s multiple comparisons test, N = 5, from 4 independent cultures. c, Gating strategy on positive control samples (CB MNC) and representative flow plots of anti-CD5 and anti-CD7 stained cells, 14 days in T cell differentiation culture in the presence of indicated STAT3 inhibitor concentrations. d, No decrease in viability (DAPI negativity) in hiPSC-derived EHT cultures treated with a STAT3 inhibitor. Mean + /- SD is depicted. N = 4, from 3 independent differentiation experiments. e, Phenotypic distribution plots of runx1/cmyb + staining in 36hpf zebrafish embryos after control and experimental morpholino injection. N = 42 and 46 (ctrl MO and cebpa MO) and N = 51 and 53 (ctrl MO and cebpb MO) from two independent experiments each. Of note, ybx1 morphants displayed severe embryonic toxicity at doses as low as 1 ng, which prevented further analysis in zebrafish. f, qRT-PCR verifying YBX1 knockdown after 72 h in dox inducible hiPSC derived HE carrying a dCAS9-KRAB fusion protein and sgRNAs against YBX1. Bars represent mean + /- SD. N = 3 independent cultures, unpaired t-test, p = 0.0115. g, CRISPRi of YBX1 does not affect the percentage of CD34 + CD45 + cells. N = 2 independent sgRNAs. Data and mean is plotted. h, Reduction of CFU potential upon CRISPRi-mediated reduction of YBX1 expression in hiPSC derived HSPCs. Bar graphs represent mean. N = 3 sgRNAs for NTC, N = 2 sgRNAs for YBX1. i, Reduced lymphoid differentiation potential (N = 4 independent cultures) upon CRISPRi-mediated reduction of YBX1 expression in hiPSC derived HSPCs. ****p < 0.0001, ***p = 0.0001. Bar graphs represent mean + /- SD. j, Representative flow cytometric plots of nascent preHSCs (VE-CAD + CD45 + ) in DMSO or APPi-treated E9.5 explant cultures. k, Quantification of phenotypic T1- and T2-preHSCs in control E10.5 AGM explant cultures (N = 9) or cultures treated with APP inhibitor (N = 8). Bars represent mean + /- SD. Unpaired t-test, two-tailed, T1-preHSCs p = 0.0722, T2-preHSCs p = 0.0258. l, Representative flow cytometric plots of E14.5 fetal liver samples from APP wild type (wt), heterozygous (het) and knockout (ko) embryos pregated on single, live, lineage negative cells. Source numerical data are available in source data. Source data
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Technical RepoRT
https://doi.org/10.1038/s41556-022-00884-1
1Department of Microbiology, Immunology and Parasitology, Federal University of Santa Catarina, Florianópolis, Brazil. 2Stem Cell Program, Boston
Children’s Hospital, Boston, MA, USA. 3Division of Hematology/Oncology, Boston Children’s Hospital and Dana Farber Cancer Institute, Boston, MA,
USA. 4Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA. 5Broad Institute of MIT and Harvard,
Cambridge, MA, USA. 6Harvard-MIT Health Sciences & Technology, Massachusetts Institute of Technology, Cambridge, MA, USA. 7Undergraduate
program in Automation and Control Engineering, Federal University of Santa Catarina, Florianópolis, Brazil. 8Graduate Program of Pharmacology, Center
for Biological Sciences, Federal University of Santa Catarina, Florianópolis, Brazil. 9Wyss Institute for Biologically Inspired Engineering, Harvard University,
Boston, MA, USA. 10Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA. 11Department of
Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA. 12Synthetic Biology Center, Massachusetts Institute of Technology,
Cambridge, MA, USA. 13These authors contributed equally: Edroaldo Lummertz da Rocha, Caroline Kubaczka. 14These authors jointly supervised this work:
Trista E. North, George Q. Daley. e-mail: trista.north@childrens.harvard.edu; George.Daley@childrens.harvard.edu
Haematopoietic stem and progenitor cell (HSPC) emer-
gence is orchestrated by a highly regulated developmental
programme. In the mid-gestation mouse embryo, HSPCs
are born in the aorta–gonad–mesonephros (AGM) region around
embryonic day (E)10.5. Recent studies have examined haematopoi-
etic development within the AGM using sorted populations to cata-
logue the transcriptional programme of haemogenic endothelium
(HE) specification and its differentiation trajectory to functional
haematopoietic stem cells (HSCs)15. Niche-derived signals are
important for HSC specification; however, a comprehensive atlas of
the cellular components of the entire AGM niche and their influ-
ence on HSC fate has been lacking. In this Technical Report, to close
this gap, we applied CellComm to obtain insights into the microen-
vironmental regulation of HSPC emergence in the AGM region. We
performed extensive experimental validation of CellComm’s predic-
tions using zebrafish and mouse embryos as well as human induced
pluripotent stem cells (iPSCs), then confirmed the roles of several
ligand–receptor interactions and downstream transcriptional regu-
lators implicated in haematopoietic development. These findings
enhance our understanding of cellular dynamics in the HSC niche
and provide further guidance for precise differentiation of iPSCs
towards HSPCs. CellComm is a valuable resource for the broader
scientific community to investigate critical cell–cell regulatory
interactions from single-cell RNA sequencing (scRNA-seq) or spa-
tial transcriptomics data.
Results
The cellular landscape of the AGM microenvironment.
CellComm is a systems biology algorithm combining transcriptome
data (scRNA-seq or spatial transcriptomics) with protein–protein
interaction networks to infer how communication between cells
activates downstream signalling pathways and transcriptional pro-
grammes dictating cell fates (Fig. 1a and Supplementary Note 1).
Briefly, CellComm infers which cell types communicate on the
basis of ligand–receptor interactions identified by calculating intra-
cluster mean expression of ligands or receptors among pairwise
combinations of cell types. If spatial transcriptomics data are avail-
able, CellComm considers co-localization of cell types or niches to
predict cell communication. By weighting protein–protein inter-
action networks using intracluster co-expression measurements,
CellComm implements an optimization algorithm to identify paths
in the interactome that connect cell surface genes to downstream
transcriptional regulators, predicting putative effectors of signalling
networks in a fully data-driven manner.
To investigate the process by which HSCs are produced
de novo during embryogenesis, we first performed scRNA-seq
CellComm infers cellular crosstalk that drives
haematopoietic stem and progenitor cell
development
Edroaldo Lummertz da Rocha 1,13, Caroline Kubaczka 2,3,4,13, Wade W. Sugden 2,3,
Mohamad Ali Najia 2,3,4,5,6, Ran Jing 2,3,4, Arianna Markel2,3,4, Zachary C. LeBlanc 2,3,
Rafael dos Santos Peixoto 7, Marcelo Falchetti 8, James J. Collins 5,6,9,10,11,12, Trista E. North2,3,14 ✉
and George Q. Daley 2,3,4,14 ✉
Intercellular communication orchestrates a multitude of physiologic and pathologic conditions. Algorithms to infer cell–cell
communication and predict downstream signalling and regulatory networks are needed to illuminate mechanisms of stem cell
differentiation and tissue development. Here, to fill this gap, we developed and applied CellComm to investigate how the aorta–
gonad–mesonephros microenvironment dictates haematopoietic stem and progenitor cell emergence. We identified key micro-
environmental signals and transcriptional networks that regulate haematopoietic development, including Stat3, Nr0b2, Ybx1
and App, and confirmed their roles using zebrafish, mouse and human models. Notably, CellComm revealed extensive crosstalk
among signalling pathways and convergence on common transcriptional regulators, indicating a resilient developmental pro-
gramme that ensures dynamic adaptation to changes in the embryonic environment. Our work provides an algorithm and data
resource for the scientific community.
NATURE CELL BIOLOGY | VOL 24 | APRIL 2022 | 579–589 | www.nature.com/naturecellbiology 579
Content courtesy of Springer Nature, terms of use apply. Rights reserved
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