Deciphering cell-cell interactions and communication from gene expression

Computational Discovery of Cancer Immunotherapy Targets by Intercellular CRISPR Screens

Cancer immunotherapy targets the interplay between immune and cancer cells. In particular, interactions between cytotoxic T lymphocytes (CTLs) and cancer cells, such as PD-1 (PDCD1) binding PD-L1 (CD274), are crucial for cancer cell clearance. However, immune checkpoint inhibitors targeting these interactions are effective only in a subset of patients, requiring the identification of novel immunotherapy targets. Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) screening in either cancer or immune cells has been employed to discover regulators of immune cell function. However, CRISPR screens in a single cell type complicate the identification of essential intercellular interactions. Further, pooled screening is associated with high noise levels. Herein, we propose intercellular CRISPR screens, a computational approach for the analysis of genome-wide CRISPR screens in every interacting cell type for the discovery of intercellular interactions as immunotherapeutic targets. We used two publicly available genome-wide CRISPR screening datasets obtained while triple-negative breast cancer (TNBC) cells and CTLs were interacting. We analyzed 4825 interactions between 1391 ligands and receptors on TNBC cells and CTLs to evaluate their effects on CTL function. Intercellular CRISPR screens discovered targets of approved drugs, a few of which were not identifiable in single datasets. To evaluate the method's performance, we used data for cytokines and costimulatory molecules as they constitute the majority of immunotherapeutic targets. Combining both CRISPR datasets improved the recall of discovering these genes relative to using single CRISPR datasets over two-fold. Our results indicate that intercellular CRISPR screens can suggest novel immunotherapy targets that are not obtained through individual CRISPR screens. The pipeline can be extended to other cancer and immune cell types to discover important intercellular interactions as potential immunotherapeutic targets.

De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with DeepLinc

Based on a deep generative model of variational graph autoencoder (VGAE), we develop a new method, DeepLinc (deep learning framework for Landscapes of Interacting Cells), for the de novo reconstruction of cell interaction networks from single-cell spatial transcriptomic data. DeepLinc demonstrates high efficiency in learning from imperfect and incomplete spatial transcriptome data, filtering false interactions, and imputing missing distal and proximal interactions. The latent representations learned by DeepLinc are also used for inferring the signature genes contributing to the cell interaction landscapes, and for reclustering the cells based on the spatially coded cell heterogeneity in complex tissues at single-cell resolution.

Breaking the Immune Complexity of the Tumor Microenvironment Using Single-Cell Technologies

Tumors are not a simple aggregate of transformed cells but rather a complicated ecosystem containing various components, including infiltrating immune cells, tumor-related stromal cells, endothelial cells, soluble factors, and extracellular matrix proteins. Profiling the immune contexture of this intricate framework is now mandatory to develop more effective cancer therapies and precise immunotherapeutic approaches by identifying exact targets or predictive biomarkers, respectively. Conventional technologies are limited in reaching this goal because they lack high resolution. Recent developments in single-cell technologies, such as single-cell RNA transcriptomics, mass cytometry, and multiparameter immunofluorescence, have revolutionized the cancer immunology field, capturing the heterogeneity of tumor-infiltrating immune cells and the dynamic complexity of tenets that regulate cell networks in the tumor microenvironment. In this review, we describe some of the current single-cell technologies and computational techniques applied for immune-profiling the cancer landscape and discuss future directions of how integrating multi-omics data can guide a new "precision oncology" advancement.

Single-Cell Transcriptomics of Liver Cancer: Hype or Insights?

Hepatocellular carcinoma (HCC) is characterized by its high degrees of both inter- and intratumoral heterogeneity. Its complex tumor microenvironment is also crucial in promoting tumor progression. Recent advances in single-cell RNA sequencing provide an important highway to characterize the underlying pathogenesis and heterogeneity of HCC in an unprecedented degree of resolution. This review discusses the up-to-date discoveries from the latest studies of HCC with respect to the strength of single-cell RNA sequencing. We discuss its use in the dissection of the landscape of the intricate HCC ecosystem and highlight the major features at cellular levels, including the malignant cells, different immune cell types, and the various cell-cell interactions, which are crucial for developing effective immunotherapies. Finally, its translational applications will be discussed. Altogether, these explorations may give us some hints at the tumor growth and progression and drug resistance and recurrence, particularly in this era of personalized medicine.

Listening in on Multicellular Communication in Human Tissue Immunology

Immune responses in human tissues rely on the concerted action of different cell types. Inter-cellular communication shapes both the function of the multicellular interaction networks and the fate of the individual cells that comprise them. With the advent of new methods to profile and experimentally perturb primary human tissues, we are now in a position to systematically identify and mechanistically dissect these cell-cell interactions and their modulators. Here, we introduce the concept of multicellular hubs, functional modules of immune responses in tissues. We outline a roadmap to discover multicellular hubs in human tissues and discuss how emerging technologies may further accelerate progress in this field.

Reconstructing physical cell interaction networks from single-cell data using Neighbor-seq

Cell-cell interactions are the fundamental building blocks of tissue organization and multicellular life. We developed Neighbor-seq, a method to identify and annotate the architecture of direct cell–cell interactions and relevant ligand–receptor signaling from the undissociated cell fractions in massively parallel single cell sequencing data. Neighbor-seq accurately identifies microanatomical features of diverse tissue types such as the small intestinal epithelium, terminal respiratory tract, and splenic white pulp. It also captures the differing topologies of cancer-immune-stromal cell communications in pancreatic and skin tumors, which are consistent with the patterns observed in spatial transcriptomic data. Neighbor-seq is fast and scalable. It draws inferences from routine single-cell data and does not require prior knowledge about sample cell-types or multiplets. Neighbor-seq provides a framework to study the organ-level cellular interactome in health and disease, bridging the gap between single-cell and spatial transcriptomics.

Characterization of cell-cell communication in autistic brains with single-cell transcriptomes

BACKGROUND

Autism spectrum disorder is a neurodevelopmental disorder, affecting 1-2% of children. Studies have revealed genetic and cellular abnormalities in the brains of affected individuals, leading to both regional and distal cell communication deficits.

METHODS

Recent application of single-cell technologies, especially single-cell transcriptomics, has significantly expanded our understanding of brain cell heterogeneity and further demonstrated that multiple cell types and brain layers or regions are perturbed in autism. The underlying high-dimensional single-cell data provides opportunities for multilevel computational analysis that collectively can better deconvolute the molecular and cellular events altered in autism. Here, we apply advanced computation and pattern recognition approaches on single-cell RNA-seq data to infer and compare inter-cell-type signaling communications in autism brains and controls.

RESULTS

Our results indicate that at a global level, there are cell-cell communication differences in autism in comparison with controls, largely involving neurons as both signaling senders and receivers, but glia also contribute to the communication disruption. Although the magnitude of changes is moderate, we find that excitatory and inhibitor neurons are involved in multiple intercellular signaling that exhibits increased strengths in autism, such as NRXN and CNTN signaling. Not all genes in the intercellular signaling pathways show differential expression, but genes in the affected pathways are enriched for axon guidance, synapse organization, neuron migration, and other critical cellular functions. Furthermore, those genes are highly connected to and enriched for genes previously associated with autism risks.

CONCLUSIONS

Overall, our proof-of-principle computational study using single-cell data uncovers key intercellular signaling pathways that are potentially disrupted in the autism brains, suggesting that more studies examining cross-cell type effects can be valuable for understanding autism pathogenesis.

Micro-patterned cell populations as advanced pharmaceutical drugs with precise functional control

Human cells are both advanced pharmaceutical drugs and 'drug deliverers'. However, functional control prior to or after cell implantation remains challenging. Micro-patterning cells through geometrically defined adhesion sites allows controlling morphogenesis, polarity, cellular mechanics, proliferation, migration, differentiation, stemness, cell-cell interactions, collective cell behavior, and likely immuno-modulatory properties. Consequently, generating micro-patterned therapeutic cells is a promising idea that has not yet been realized and few if any steps have been undertaken in this direction. This review highlights potential therapeutic applications, summarizes comprehensively the many cell functions that have been successfully controlled through micro-patterning, details the established micro-pattern designs, introduces the available fabrication technologies to the non-specialized reader, and suggests a quality evaluation score. Such a broad review is not yet available but would facilitate the manufacturing of therapeutically patterned cell populations using micro-patterned cell-instructive biomaterials for improved functional control as drug delivery systems in the context of cells as pharmaceutical products.

Osteocytes in bone aging: Advances, challenges, and future perspectives

Osteocytes play a critical role in maintaining bone homeostasis and in regulating skeletal response to hormones and mechanical loading. Substantial evidence have demonstrated that osteocytes and their lacunae exhibit morphological changes in aged bone, indicating the underlying involvement of osteocytes in bone aging. Notably, recent studies have deciphered aged osteocytes to have characteristics such as impaired mechanosensitivity, accumulated cellular senescence, dysfunctional perilacunar/canalicular remodeling, and degenerated lacuna-canalicular network. However, detailed molecular mechanisms of osteocytes remain unclear. Nonetheless, osteocyte transcriptomes analyzed via advanced RNA sequencing (RNA-seq) techniques have identified several bone aging-related genes and signaling pathways, such as Wnt, Bmp/TGF, and Jak-STAT. Moreover, inflammation, immune dysfunction, energy shortage, and impaired hormone responses possibly affect osteocytes in age-related bone deterioration. In this review, we summarize the hallmarks of aging bone and osteocytes and discuss osteocytic mechanisms in age-related bone loss and impaired bone quality. Furthermore, we provide insights into the challenges faced and their possible solutions when investigating osteocyte transcriptomes. We also highlight that single-cell RNA-seq can decode transcriptomic messages in aged osteocytes; therefore, this technique can promote novel single cell-based investigations in osteocytes once a well-established standardized protocol specific for osteocytes is developed. Interestingly, improved understanding of osteocytic mechanisms have helped identify promising targets and effective therapies for aging-related osteoporosis and fragile fractures.

Mapping and Validation of scRNA-Seq-Derived Cell-Cell Communication Networks in the Tumor Microenvironment

Recent advances in single-cell technologies, particularly single-cell RNA-sequencing (scRNA-seq), have permitted high throughput transcriptional profiling of a wide variety of biological systems. As scRNA-seq supports inference of cell-cell communication, this technology has and continues to anchor groundbreaking studies into the efficacy and mechanism of novel immunotherapies for cancer treatment. In this review, we will highlight methods developed to infer inter- and intracellular signaling from scRNA-seq and discuss how they have contributed to studies of immunotherapeutic intervention in the tumor microenvironment (TME). However, a central challenge remains in validating the hypothesized cell-cell interactions. Therefore, this review will also cover strategies for integration of these scRNA-seq-derived interaction networks with existing experimental and computational approaches. Integration of these networks with imaging, protein secretion measurements, and network analysis and mathematical modeling tools addresses challenges that remain with scRNA-seq to enhance studies of immunosuppressive and immunotherapy-altered signaling in the TME.

DualGCN: a dual graph convolutional network model to predict cancer drug response

Background

Drug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future.

Results

We present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein–protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data.

Conclusions

The proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04664-4.

Immunologic Gene Sets Reveal Features of the Tumor Immune Microenvironment and Predict Prognosis and Immunotherapy Response: A Pan-Cancer Analysis

In the treatment of cancer, anti-programmed cell death-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) immunotherapy has achieved unprecedented clinical success. However, the significant response to these therapies is limited to a small number of patients. This study aimed to predict immunotherapy response and prognosis using immunologic gene sets (IGSs). The enrichment scores of 4,872 IGSs in 348 patients with metastatic urothelial cancer treated with anti-PD-L1 therapy were computed using gene set variation analysis (GSVA). An IGS-based classification (IGSC) was constructed using a nonnegative matrix factorization (NMF) approach. An IGS-based risk prediction model (RPM) was developed using the least absolute shrinkage and selection operator (LASSO) method. The IMvigor210 cohort was divided into three distinct subtypes, among which subtype 2 had the best prognosis and the highest immunotherapy response rate. Subtype 2 also had significantly higher PD-L1 expression, a higher proportion of the immune-inflamed phenotype, and a higher tumor mutational burden (TMB). An RPM was constructed using four gene sets, and it could effectively predict prognosis and immunotherapy response in patients receiving anti-PD-L1 immunotherapy. Pan-cancer analyses also demonstrated that the RPM was capable of accurate risk stratification across multiple cancer types, and RPM score was significantly associated with TMB, microsatellite instability (MSI), CD8+ T-cell infiltration, and the expression of cytokines interferon-γ (IFN-γ), transforming growth factor-β (TGF-β) and tumor necrosis factor-α (TNF-α), which are key predictors of immunotherapy response. The IGSC strengthens our understanding of the diverse biological processes in tumor immune microenvironment, and the RPM can be a promising biomarker for predicting the prognosis and response in cancer immunotherapy.

Afatinib and Pembrolizumab for Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma (ALPHA Study): A Phase II Study with Biomarker Analysis

PURPOSE

EGFR pathway inhibition may promote anti-programmed cell death protein 1 (PD-1) responses in preclinical models, but how EGFR inhibition affects tumor antigen presentation during anti-PD-1 monotherapy in humans remain unknown. We hypothesized that afatinib, an irreversible EGFR tyrosine kinase inhibitor, would improve outcomes in patients treated with pembrolizumab for recurrent or metastatic head and neck squamous cell carcinoma (HNSCC) by promoting antigen presentation and immune activation in the tumor microenvironment.

PATIENTS AND METHODS

The ALPHA study (NCT03695510) was a single-arm, Phase II study with Simon's 2-stage design. Afatinib and pembrolizumab were administered to patients with platinum-refractory, recurrent, or metastatic HNSCC. The primary endpoint was the objective response rate (ORR). The study applied gene expression analysis using a NanoString PanCancer Immune Profiling Panel and next-generation sequencing using FoundationOne CDx.

RESULTS

From January 2019 to March 2020, the study enrolled 29 eligible patients. Common treatment-related adverse events were skin rash (75.9%), diarrhea (58.6%), and paronychia (44.8%). Twelve patients (41.4%) had an objective partial response to treatment. The median progression-free survival was 4.1 months, and the median overall survival was 8.9 months. In a paired tissue analysis, afatinib-pembrolizumab were found to upregulate genes involved in antigen presentation, immune activation, and natural killer cell-mediated cytotoxicity. Unaltered methylthioadenosine phosphorylase and EGFR amplification may predict the clinical response to the therapy.

CONCLUSIONS

Afatinib may augment pembrolizumab therapy and improve the ORR in patients with HNSCC. Bioinformatics analysis suggested the enhancement of antigen presentation machinery in the tumor microenvironment.

A Toolkit for Profiling the Immune Landscape of Pediatric Central Nervous System Malignancies

The prognosis of pediatric central nervous system (CNS) malignancies remains dismal due to limited treatment options, resulting in high mortality rates and long-term morbidities. Immunotherapies, including checkpoint inhibition, cancer vaccines, engineered T cell therapies, and oncolytic viruses, have promising results in some hematological and solid malignancies, and are being investigated in clinical trials for various high-grade CNS malignancies. However, the role of the tumor immune microenvironment (TIME) in CNS malignancies is mostly unknown for pediatric cases. In order to successfully implement immunotherapies and to eventually predict which patients would benefit from such treatments, in-depth characterization of the TIME at diagnosis and throughout treatment is essential. In this review, we provide an overview of techniques for immune profiling of CNS malignancies, and detail how they can be utilized for different tissue types and studies. These techniques include immunohistochemistry and flow cytometry for quantifying and phenotyping the infiltrating immune cells, bulk and single-cell transcriptomics for describing the implicated immunological pathways, as well as functional assays. Finally, we aim to describe the potential benefits of evaluating other compartments of the immune system implicated by cancer therapies, such as cerebrospinal fluid and blood, and how such liquid biopsies are informative when designing immune monitoring studies. Understanding and uniformly evaluating the TIME and immune landscape of pediatric CNS malignancies will be essential to eventually integrate immunotherapy into clinical practice.

An Open-Source Modular Framework for Automated Pipetting and Imaging Applications

The number of samples in biological experiments is continuously increasing, but complex protocols and human error in many cases lead to suboptimal data quality and hence difficulties in reproducing scientific findings. Laboratory automation can alleviate many of these problems by precisely reproducing machine-readable protocols. These instruments generally require high up-front investments, and due to the lack of open application programming interfaces (APIs), they are notoriously difficult for scientists to customize and control outside of the vendor-supplied software. Here, automated, high-throughput experiments are demonstrated for interdisciplinary research in life science that can be replicated on a modest budget, using open tools to ensure reproducibility by combining the tools OpenFlexure, Opentrons, ImJoy, and UC2. This automated sample preparation and imaging pipeline can easily be replicated and established in many laboratories as well as in educational contexts through easy-to-understand algorithms and easy-to-build microscopes. Additionally, the creation of feedback loops, with later pipetting or imaging steps depending on the analysis of previously acquired images, enables the realization of fully autonomous "smart" microscopy experiments. All documents and source files are publicly available to prove the concept of smart lab automation using inexpensive, open tools. It is believed this democratizes access to the power and repeatability of automated experiments.

Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder

Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we develop a graph attention auto-encoder framework STAGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. To better characterize the spatial similarity at the boundary of spatial domains, STAGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions. We validate STAGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STAGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STAGATE could be extended to multiple consecutive sections to reduce batch effects between sections and extracting three-dimensional (3D) expression domains from the reconstructed 3D tissue effectively.

Molecular pathogenesis and systemic therapies for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains one of the most prevalent and deadliest cancers. The poor outcome associated with HCC is dramatically changing due to the advent of effective systemic therapies. Here we discuss the molecular pathogenesis of HCC, molecular classes and determinants of heterogeneity. In addition, effective single-agent and combination systemic therapies involving immunotherapies as standard of care are analyzed. Finally, we propose a flowchart of sequential therapies, explore mechanisms of resistance and address the need for predictive biomarkers.

Allostery, and how to define and measure signal transduction

Here we ask: What is productive signaling? How to define it, how to measure it, and most of all, what are the parameters that determine it? Further, what determines the strength of signaling from an upstream to a downstream node in a specific cell? These questions have either not been considered or not entirely resolved. The requirements for the signal to propagate downstream to activate (repress) transcription have not been considered either. Yet, the questions are pivotal to clarify, especially in diseases such as cancer where determination of signal propagation can point to cell proliferation and to emerging drug resistance, and to neurodevelopmental disorders, such as RASopathy, autism, attention-deficit/hyperactivity disorder (ADHD), and cerebral palsy. Here we propose a framework for signal transduction from an upstream to a downstream node addressing these questions. Defining cellular processes, experimentally measuring them, and devising powerful computational AI-powered algorithms that exploit the measurements, are essential for quantitative science.

Antigen-Specific T Cell Detection via Photocatalytic Proximity Cell Labeling (PhoXCELL)

Quantitative detection and characterization of antigen-specific T cells are crucial to our understanding of immune responses as well as the development of new immunotherapies. Herein, we report a spatiotemporally resolved method for the detection and quantification of cell-cell interactions via Photocatalytic proXimity CELl Labeling (PhoXCELL). The biocompatible photosensitizer dibromofluorescein (DBF) was leveraged and optimized as a nongenetic alternative of enzymatic approaches for efficient generation of singlet oxygen upon photoirradiation (520 nm) on the cell surface, which allowed the subsequent labeling of nearby oxidized proteins with primary aliphatic amine-based probes. We demonstrated that DBF-functionalized dendritic cells (DCs) could spatiotemporally label interacting T cells in immune synapses via rapid photoirradiation with quantitatively discriminated interaction strength, which revealed distinct gene signatures for T cells that strongly interact with antigen-pulsed DCs. Furthermore, we employed PhoXCELL to simultaneously detect tumor antigen-specific CD8⁺ as well as CD4⁺ T cells from tumor-infiltrating lymphocytes and draining lymph nodes in murine tumor models, enabling PhoXCELL as a powerful platform to identify antigen-specific T cells in T cell receptor (TCR)-relevant personal immunotherapy.

Identify, quantify and characterize cellular communication from single-cell RNA sequencing data with scSeqComm

MOTIVATION

Recently, single-cell RNA-seq (scRNA-seq) data have been used to study cellular communication. Most bioinformatics methods infer only the intercellular signaling between groups of cells, mainly exploiting ligand-receptor expression levels. Only few methods consider the entire intercellular + intracellular signaling, mainly inferring lists/networks of signaling involved genes.

RESULTS

Here, we present scSeqComm, a computational method to identify and quantify the evidence of ongoing intercellular and intracellular signaling from scRNA-seq data, and at the same time providing a functional characterization of the inferred cellular communication. The possibility to quantify the evidence of ongoing communication assists the prioritization of the results, while the combined evidence of both intercellular and intracellular signaling increase the reliability of inferred communication. The application to a scRNA-seq dataset of tumor microenvironment, the agreement with independent bioinformatics analysis, the validation using spatial transcriptomics data and the comparison with state-of-the-art intercellular scoring schemes confirmed the robustness and reliability of the proposed method.

AVAILABILITY AND IMPLEMENTATION

scSeqComm R package is freely available at https://gitlab.com/sysbiobig/scseqcomm and https://sysbiobig.dei.unipd.it/software/#scSeqComm. Submitted software version and test data are available in Zenodo, at https://dx.doi.org/10.5281/zenodo.5833298.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A light-gated transcriptional recorder for detecting cell-cell contacts

Technologies for detecting cell-cell contacts are powerful tools for studying a wide range of biological processes, from neuronal signaling to cancer-immune interactions within the tumor microenvironment. Here, we report TRACC (Transcriptional Readout Activated by Cell-cell Contacts), a GPCR-based transcriptional recorder of cellular contacts, which converts contact events into stable transgene expression. TRACC is derived from our previous protein-protein interaction recorders, SPARK (Kim et al., 2017) and SPARK2 (Kim et al., 2019), reported in this journal. TRACC incorporates light gating via the light-oxygen-voltage-sensing (LOV) domain, which provides user-defined temporal control of tool activation and reduces background. We show that TRACC detects cell-cell contacts with high specificity and sensitivity in mammalian cell culture and that it can be used to interrogate interactions between neurons and glioma, a form of brain cancer.

Reconstructing codependent cellular cross-talk in lung adenocarcinoma using REMI

Cellular cross-talk in tissue microenvironments is fundamental to normal and pathological biological processes. Global assessment of cell-cell interactions (CCIs) is not yet technically feasible, but computational efforts to reconstruct these interactions have been proposed. Current computational approaches that identify CCI often make the simplifying assumption that pairwise interactions are independent of one another, which can lead to reduced accuracy. We present REMI (REgularized Microenvironment Interactome), a graph-based algorithm that predicts ligand-receptor (LR) interactions by accounting for LR dependencies on high-dimensional, small-sample size datasets. We apply REMI to reconstruct the human lung adenocarcinoma (LUAD) interactome from a bulk flow-sorted RNA sequencing dataset, then leverage single-cell transcriptomics data to increase the cell type resolution and identify LR prognostic signatures among tumor-stroma-immune subpopulations. We experimentally confirmed colocalization of CTGF:LRP6 among malignant cell subtypes as an interaction predicted to be associated with LUAD progression. Our work presents a computational approach to reconstruct interactomes and identify clinically relevant CCIs.

Dynamic EGFR interactomes reveal differential association of signaling modules with wildtype and Exon19-del EGFR in NSCLC cell lines

Protein-protein interaction networks (PPIs) govern the majority of biological processes, but how oncogenic mutations impact these interactions and their functions at a network scale is poorly understood. Mutations of epidermal growth factor receptor (EGFR) in non-small cell lung cancer (NSCLC) is a pre-requisition for EGFR tyrosine kinase inhibitor (TKI) treatment. Identification of interaction partners that bind to mutated EGFR can help understand the mechanism of action and pathways that mediate drug resistance. In this study, we characterized the dynamic interaction network of a pair of EGFR wildtype and mutant NSCLC cell lines. We performed immunoprecipitation of endogenous EGFR at various time points following EGF treatment and analyzed the associated proteins by quantitative mass spectrometry. Our results showed that the core signaling modules and key downstream pathways are maintained in the mutant cell line, but receptor internalization and intracellular trafficking in the mutant is delayed. Furthermore, we identified mutant EGFR-associated proteins that could affect EGFR functions in lung adenocarcinoma. SIGNIFICANCE: We analyzed the dynamic EGFR interaction network in NSCLC cell lines expressing wild-type and mutant EGFR. By comparing the similarities and differences in the EGFR proteome, we gained a better understanding of EGFR signal transduction network, and identified new factors for further functional characterizations and clinical significance assessment.

Deciphering tissue structure and function using spatial transcriptomics

The rapid development of spatial transcriptomics (ST) techniques has allowed the measurement of transcriptional levels across many genes together with the spatial positions of cells. This has led to an explosion of interest in computational methods and techniques for harnessing both spatial and transcriptional information in analysis of ST datasets. The wide diversity of approaches in aim, methodology and technology for ST provides great challenges in dissecting cellular functions in spatial contexts. Here, we synthesize and review the key problems in analysis of ST data and methods that are currently applied, while also expanding on open questions and areas of future development.

TraSig: inferring cell-cell interactions from pseudotime ordering of scRNA-Seq data

A major advantage of single cell RNA-sequencing (scRNA-Seq) data is the ability to reconstruct continuous ordering and trajectories for cells. Here we present TraSig, a computational method for improving the inference of cell-cell interactions in scRNA-Seq studies that utilizes the dynamic information to identify significant ligand-receptor pairs with similar trajectories, which in turn are used to score interacting cell clusters. We applied TraSig to several scRNA-Seq datasets and obtained unique predictions that improve upon those identified by prior methods. Functional experiments validate the ability of TraSig to identify novel signaling interactions that impact vascular development in liver organoids.Software https://github.com/doraadong/TraSig .

Systems Biology of Gut Microbiota-Human Receptor Interactions: Toward Anti-inflammatory Probiotics

The incidence and prevalence of inflammatory disorders have increased globally, and is projected to double in the next decade. Gut microbiome-based therapeutics have shown promise in ameliorating chronic inflammation. However, they are largely experimental, context- or strain-dependent and lack a clear mechanistic basis. This hinders precision probiotics and poses significant risk, especially to individuals with pre-existing conditions. Molecules secreted by gut microbiota act as ligands to several health-relevant receptors expressed in human gut, such as the G-protein coupled receptors (GPCRs), Toll-like receptor 4 (TLR4), pregnane X receptor (PXR), and aryl hydrocarbon receptor (AhR). Among these, the human AhR expressed in different tissues exhibits anti-inflammatory effects and shows activity against a wide range of ligands produced by gut bacteria. However, different AhR ligands induce varying host responses and signaling in a tissue/organ-specific manner, which remain mostly unknown. The emerging systems biology paradigm, with its powerful in silico tool repertoire, provides opportunities for comprehensive and high-throughput strain characterization. In particular, combining metabolic models with machine learning tools can be useful to delineate tissue and ligand-specific signaling and thus their causal mechanisms in disease and health. The knowledge of such a mechanistic basis is indispensable to account for strain heterogeneity and actualize precision probiotics.

Synovial single-cell heterogeneity, zonation and interactions: a patchwork of effectors in arthritis

Despite extensive research, there is still no treatment that would lead to remission in all patients with rheumatoid arthritis as our understanding of the affected site, the synovium, is still incomplete. Recently, single-cell technologies helped to decipher the cellular heterogeneity of the synovium; however, certain synovial cell populations, such as endothelial cells or peripheral neurons, remain to be profiled on a single-cell level. Furthermore, associations between certain cellular states and inflammation were found; whether these cells cause the inflammation remains to be answered. Similarly, cellular zonation and interactions between individual effectors in the synovium are yet to be fully determined. A deeper understanding of cell signalling and interactions in the synovium is crucial for a better design of therapeutics with the goal of complete remission in all patients.

Fate-mapping mice: new tools and technology for immune discovery

The fate-mapping mouse has become an essential tool in the immunologist's toolbox. Although traditionally used by developmental biologists to trace the origins of cells, immunologists are turning to fate-mapping to better understand the development and function of immune cells. Thus, an expansion in the variety of fate-mapping mouse models has occurred to answer fundamental questions about the immune system. These models are also being combined with new genetic tools to study cancer, infection, and autoimmunity. In this review, we summarize different types of fate-mapping mice and describe emerging technologies that might allow immunologists to leverage this valuable tool and expand our functional knowledge of the immune system.

How Single-Cell Technologies Have Provided New Insights Into Atherosclerosis

The development of innovative single-cell technologies has allowed the high-dimensional transcriptomic and proteomic profiling of individual blood and tissue cells. Recent single-cell studies revealed a new cellular heterogeneity of atherosclerotic plaque tissue and allowed a better understanding of distinct immune functional states in the context of atherosclerosis. In this brief review, we describe how single-cell technologies have shed a new light on the cellular composition of atherosclerotic plaques, and their response to diet perturbations or genetic manipulation in mouse models of atherosclerosis. We discuss how single-cell RNA sequencing, cellular indexing of transcriptomes and epitopes by sequencing, transposase-accessible chromatin with high-throughput sequencing, and cytometry by time-of-flight platforms have empowered the identification of discrete immune, endothelial, and smooth muscle cell alterations in atherosclerosis progression and regression. Finally, we review how single-cell approaches have allowed mapping the cellular and molecular composition of human atherosclerotic plaques and the discovery of new immune alterations in plaques from patients with stroke.

The role of cell geometry and cell-cell communication in gradient sensing

Cells can measure shallow gradients of external signals to initiate and accomplish a migration or a morphogenetic process. Recently, starting from mathematical models like the local-excitation global-inhibition (LEGI) model and with the support of empirical evidence, it has been proposed that cellular communication improves the measurement of an external gradient. However, the mathematical models that have been used have over-simplified geometries (e.g., they are uni-dimensional) or assumptions about cellular communication, which limit the possibility to analyze the gradient sensing ability of more complex cellular systems. Here, we generalize the existing models to study the effects on gradient sensing of cell number, geometry and of long- versus short-range cellular communication in 2D systems representing epithelial tissues. We find that increasing the cell number can be detrimental for gradient sensing when the communication is weak and limited to nearest neighbour cells, while it is beneficial when there is long-range communication. We also find that, with long-range communication, the gradient sensing ability improves for tissues with more disordered geometries; on the other hand, an ordered structure with mostly hexagonal cells is advantageous with nearest neighbour communication. Our results considerably extend the current models of gradient sensing by epithelial tissues, making a step further toward predicting the mechanism of communication and its putative mediator in many biological processes.

Computational exploration of cellular communication in skin from emerging single-cell and spatial transcriptomic data

Tissue development and homeostasis require coordinated cell-cell communication. Recent advances in single-cell sequencing technologies have emerged as a revolutionary method to reveal cellular heterogeneity with unprecedented resolution. This offers a great opportunity to explore cell-cell communication in tissues systematically and comprehensively, and to further identify signaling mechanisms driving cell fate decisions and shaping tissue phenotypes. Using gene expression information from single-cell transcriptomics, several computational tools have been developed for inferring cell-cell communication, greatly facilitating analysis and interpretation. However, in single-cell transcriptomics, spatial information of cells is inherently lost. Given that most cell signaling events occur within a limited distance in tissues, incorporating spatial information into cell-cell communication analysis is critical for understanding tissue organization and function. Spatial transcriptomics provides spatial location of cell subsets along with their gene expression, leading to new directions for leveraging spatial information to develop computational approaches for cell-cell communication inference and analysis. These computational approaches have been successfully applied to uncover previously unrecognized mechanisms of intercellular communication within various contexts and across organ systems, including the skin, a formidable model to study mechanisms of cell-cell communication due to the complex interactions between the different cell populations that comprise it. Here, we review emergent cell-cell communication inference tools using single-cell transcriptomics and spatial transcriptomics, and highlight the biological insights gained by applying these computational tools to exploring cellular communication in skin development, homeostasis, disease and aging, as well as discuss future potential research avenues.

High Throughput scRNA-Seq Provides Insights Into Leydig Cell Senescence Induced by Experimental Autoimmune Orchitis: A Prominent Role of Interstitial Fibrosis and Complement Activation

Leydig cells (Lc), located in the interstitial space of the testis between seminiferous tubules, produce 95% of testosterone in male individuals, which is pivotal for male sexual differentiation, spermatogenesis, and maintenance of the male secondary sex characteristics. Lc are prone to senescence in aging testes, resulting in compromised androgen synthesis capability upon aging. However, little is known about whether Lc undergo senescence in a chronic inflammatory environment. To investigate this question, mouse models of experimental autoimmune orchitis (EAO) were used, and Lc were analyzed by high throughput scRNA-Seq. Data were screened and analyzed by correlating signaling pathways with senescence, apoptosis, androgen synthesis, and cytokine/chemokine signaling pathways. EAO did induce Lc senescence, and Lc senescence in turn antagonized androgen synthesis. Based on the correlation screening of pathways inducing Lc senescence, a plethora of pathways were found to play potential roles in triggering Lc senescence during EAO, among which the Arf6 and angiopoietin receptor pathways were highly correlated with senescence signature. Notably, complement and interstitial fibrosis activated by EAO worsened Lc senescence and strongly antagonized androgen synthesis. Furthermore, most proinflammatory cytokines enhanced both senescence and apoptosis in Lc and spermatogonia (Sg) during EAO, and proinflammatory cytokine antagonism of the glutathione metabolism pathway may be key in inducing cellular senescence during EAO.

Tissue Schematics: A framework to decipher tissue architecture and assembly

High-parameter spatial proteomics provide unprecedented opportunities to investigate how tissue architectures are assembled. In an article in this issue of Cell Systems, Bhate et al. present "Tissue Schematics," a conceptual framework and computational approach to decipher the rules of tissue assembly.

The Applications of Single-Cell RNA Sequencing in Atherosclerotic Disease

Atherosclerosis still is the primary cause of death worldwide. Our characterization of the atherosclerotic lesion is mainly rooted in definitions based on pathological descriptions. We often speak in absolutes regarding plaque phenotypes: vulnerable vs. stable plaques or plaque rupture vs. plaque erosion. By focusing on these concepts, we may have oversimplified the atherosclerotic disease and its mechanisms. The widely used definitions of pathology-based plaque phenotypes can be fine-tuned with observations made with various -omics techniques. Recent advancements in single-cell transcriptomics provide the opportunity to characterize the cellular composition of the atherosclerotic plaque. This additional layer of information facilitates the in-depth characterization of the atherosclerotic plaque. In this review, we discuss the impact that single-cell transcriptomics may exert on our current understanding of atherosclerosis.

Deciphering Cell-Cell Communication in Abdominal Aortic Aneurysm From Single-Cell RNA Transcriptomic Data

Cell-cell communication coordinates cellular differentiation, tissue homeostasis, and immune responses in states of health and disease. In abdominal aortic aneurysm (AAA), a relatively common and potentially life-threatening vascular disease, intercellular communications between multiple cell types are not fully understood. In this study, we analyzed published single-cell RNA sequencing (scRNA-seq) datasets generated from the murine CaCl2 model, perivascular elastase model, Angiotensin II model, and human AAA using bioinformatic approaches. We inferred the intercellular communication network in each experimental AAA model and human AAA and predicted commonly altered signaling pathways, paying particular attention to thrombospondin (THBS) signaling between different cell populations. Together, our analysis inferred intercellular signaling in AAA based on single-cell transcriptomics. This work provides important insight into cell-cell communications in AAA and has laid the groundwork for future experimental investigations that can elucidate the cell signaling pathways driving AAA.

Single-Cell Technologies to Decipher the Immune Microenvironment in Myeloid Neoplasms: Perspectives and Opportunities

Myeloid neoplasms (MN) are heterogeneous clonal disorders arising from the expansion of hematopoietic stem and progenitor cells. In parallel with genetic and epigenetic dynamics, the immune system plays a critical role in modulating tumorigenesis, evolution and therapeutic resistance at the various stages of disease progression. Single-cell technologies represent powerful tools to assess the cellular composition of the complex tumor ecosystem and its immune environment, to dissect interactions between neoplastic and non-neoplastic components, and to decipher their functional heterogeneity and plasticity. In addition, recent progress in multi-omics approaches provide an unprecedented opportunity to study multiple molecular layers (DNA, RNA, proteins) at the level of single-cell or single cellular clones during disease evolution or in response to therapy. Applying single-cell technologies to MN holds the promise to uncover novel cell subsets or phenotypic states and highlight the connections between clonal evolution and immune escape, which is crucial to fully understand disease progression and therapeutic resistance. This review provides a perspective on the various opportunities and challenges in the field, focusing on key questions in MN research and discussing their translational value, particularly for the development of more efficient immunotherapies.

Assessment of Surface Glycan Diversity on Extracellular Vesicles by Lectin Microarray and Glycoengineering Strategies for Drug Delivery Applications

Extracellular vesicles (EVs) are released by all types of mammalian cells for cell-cell communication. In this study, surface glycans on EVs are compared in terms of their cell type, size, and isolation method to examine whether EV glycan profiles by lectin microarray can be used to define EV subpopulations. Moreover, EVs are glycoengineered with four distinctive surface glycan patterns and evaluated their cellular uptake efficiencies for potential drug delivery applications. Both similarities and differences in glycan patterns are identified on EVs obtained under each experimental condition. EV size- and isolation method-dependent lectin-binding patterns are observed. Moreover, cellular uptake behaviors of EVs are affected by EV glycan profiles and acceptor cells. The in vivo biodistribution of EVs is also dependent on their glycan profile. These results suggest that EV surface glycans are a potential novel indicator of EV heterogeneity, and glycoengineering is a useful approach to regulate cell-EV interactions for biomedical applications.

Single-cell RNA sequencing to track novel perspectives in HSC heterogeneity

As the importance of cell heterogeneity has begun to be emphasized, single-cell sequencing approaches are rapidly adopted to study cell heterogeneity and cellular evolutionary relationships of various cells, including stem cell populations. The hematopoietic stem and progenitor cell (HSPC) compartment contains HSC hematopoietic stem cells (HSCs) and distinct hematopoietic cells with different abilities to self-renew. These cells perform their own functions to maintain different hematopoietic lineages. Undeniably, single-cell sequencing approaches, including single-cell RNA sequencing (scRNA-seq) technologies, empower more opportunities to study the heterogeneity of normal and pathological HSCs. In this review, we discuss how these scRNA-seq technologies contribute to tracing origin and lineage commitment of HSCs, profiling the bone marrow microenvironment and providing high-resolution dissection of malignant hematopoiesis, leading to exciting new findings in HSC biology.

Analysis and Visualization of Spatial Transcriptomic Data

Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information. Spatial transcriptomics is a recent technological innovation that measures transcriptomic information while preserving spatial information. Spatial transcriptomic data can be generated in several ways. RNA molecules are measured by in situ sequencing, in situ hybridization, or spatial barcoding to recover original spatial coordinates. The inclusion of spatial information expands the range of possibilities for analysis and visualization, and spurred the development of numerous novel methods. In this review, we summarize the core concepts of spatial genomics technology and provide a comprehensive review of current analysis and visualization methods for spatial transcriptomics.

Optophysiology: Illuminating cell physiology with optogenetics

Optogenetics combines light and genetics to enable precise control of living cells, tissues, and organisms with tailored functions. Optogenetics has the advantages of noninvasiveness, rapid responsiveness, tunable reversibility, and superior spatiotemporal resolution. Following the initial discovery of microbial opsins as light-actuated ion channels, a plethora of naturally occurring or engineered photoreceptors or photosensitive domains that respond to light at varying wavelengths has ushered in the next chapter of optogenetics. Through protein engineering and synthetic biology approaches, genetically-encoded photoswitches can be modularly engineered into protein scaffolds or host cells to control a myriad of biological processes, as well as to enable behavioral control and disease intervention in vivo. Here, we summarize these optogenetic tools on the basis of their fundamental photochemical properties to better inform the chemical basis and design principles. We also highlight exemplary applications of opsin-free optogenetics in dissecting cellular physiology (designated "optophysiology"), and describe the current progress, as well as future trends, in wireless optogenetics, which enables remote interrogation of physiological processes with minimal invasiveness. This review is anticipated to spark novel thoughts on engineering next-generation optogenetic tools and devices that promise to accelerate both basic and translational studies.

Single-cell Transcriptomic Profiling of the Hypothalamic Median Eminence during Aging

Aging is a slow and progressive natural process that compromises the normal functions of cells, tissues, organs and systems. The aging of the hypothalamic median eminence (ME), a structural gate linking neural and endocrine systems, may impair hormone release, energy homeostasis and central sensing of circulating molecules, leading to systemic and reproductive aging. However, the molecular and cellular features of ME aging remain largely unknown. Here we describe the transcriptional landscape of young and middle-aged mouse ME at single-cell resolution, revealing the common and cell-type-specific transcriptional changes with age. The transcriptional changes in cell-intrinsic programs, cell-cell crosstalk and cell-extrinsic factors highlight five molecular features of ME aging and also implicate several potentially druggable targets at cellular, signaling and molecular levels. Importantly, our results suggest that vascular and leptomeningeal cells (VLMCs) may lead the asynchronized aging process among diverse cell types and drive local inflammation and cellular senescence via a unique secretome. Together, our study uncovers how intrinsic and extrinsic features of each cell type in the hypothalamic ME are changed by the aging process, which will facilitate our understanding of brain aging and provide clues for efficient anti-aging intervention at the middle-aged stage.

InterCellar enables interactive analysis and exploration of cell-cell communication in single-cell transcriptomic data

Deciphering cell-cell communication is a key step in understanding the physiology and pathology of multicellular systems. Recent advances in single-cell transcriptomics have contributed to unraveling the cellular composition of tissues and enabled the development of computational algorithms to predict cellular communication mediated by ligand-receptor interactions. Despite the existence of various tools capable of inferring cell-cell interactions from single-cell RNA sequencing data, the analysis and interpretation of the biological signals often require deep computational expertize. Here we present InterCellar, an interactive platform empowering lab-scientists to analyze and explore predicted cell-cell communication without requiring programming skills. InterCellar guides the biological interpretation through customized analysis steps, multiple visualization options, and the possibility to link biological pathways to ligand-receptor interactions. Alongside convenient data exploration features, InterCellar implements data-driven analyses including the possibility to compare cell-cell communication from multiple conditions. By analyzing COVID-19 and melanoma cell-cell interactions, we show that InterCellar resolves data-driven patterns of communication and highlights molecular signals through the integration of biological functions and pathways. We believe our user-friendly, interactive platform will help streamline the analysis of cell-cell communication and facilitate hypothesis generation in diverse biological systems.

The role of exosomes in inter-cellular and inter-organ communication of the peripheral nervous system

Exosomes, nano-sized extracellular vesicles, are produced via the endosomal pathway and released in the extracellular space upon fusion of multivesicular bodies with the plasma membrane. Recent evidence shows that these extracellular vesicles play a key role in cell-to-cell communication. Exosomes transport bioactive proteins, messenger RNA (mRNAs) and microRNA (miRNAs) in an active form to adjacent cells or to distant organs. In this review, we focus on the role of exosomes in peripheral nerve maintenance and repair, as well as peripheral nerve/organ crosstalk, and discuss the potential benefits of exploiting exosomes for treating PNS injuries. In addition, we will highlight the emerging role of exosomes as new important vehicles for physiological systemic crosstalk failures, which could lead to organ dysfunction during neuroinflammation or aging.

Single-Cell RNA Sequencing of Human Corpus Cavernosum Reveals Cellular Heterogeneity Landscapes in Erectile Dysfunction

PURPOSE

To assess the diverse cell populations of human corpus cavernosum in patients with severe erectile dysfunction (ED) at the single-cell level.

METHODS

Penile tissues collected from three patients were subjected to single-cell RNA sequencing using the BD Rhapsody™ platform. Common bioinformatics tools were used to analyze cellular heterogeneity and gene expression profiles from generated raw data, including the packages Seurat, Monocle, and CellPhoneDB.

RESULTS

Disease-related heterogeneity of cell types was determined in the cavernous tissue such as endothelial cells (ECs), smooth muscle cells, fibroblasts, and immune cells. Reclustering analysis of ECs identified an arteriole ECs subcluster and another one with gene signatures of fibroblasts. The proportion of fibroblasts was higher than the other cell populations and had the most significant cellular heterogeneity, in which a distinct subcluster co-expressed endothelial markers. The transition trajectory of differentiation from smooth muscle cells into fibroblasts was depicted using the pseudotime analysis, suggesting that the expansion of corpus cavernosum is possibly compromised as a result of fibrosis. Cell-cell communications among ECs, smooth muscle cells, fibroblasts, and macrophages were robust, which indicated that inflammation may also have a crucial role in the development of ED.

CONCLUSIONS

Our study has demonstrated a comprehensive single-cell atlas of cellular components in human corpus cavernosum of ED, providing in-depth insights into the pathogenesis. Future research is warranted to explore disease-specific alterations for individualized treatment of ED.

Dynamic inference of cell developmental complex energy landscape from time series single-cell transcriptomic data

Time series single-cell RNA sequencing (scRNA-seq) data are emerging. However, dynamic inference of an evolving cell population from time series scRNA-seq data is challenging owing to the stochasticity and nonlinearity of the underlying biological processes. This calls for the development of mathematical models and methods capable of reconstructing cellular dynamic transition processes and uncovering the nonlinear cell-cell interactions. In this study, we present GraphFP, a nonlinear Fokker-Planck equation on graph based model and dynamic inference framework, with the aim of reconstructing the cell state-transition complex potential energy landscape from time series single-cell transcriptomic data. The free energy of our model explicitly takes into account of the cell-cell interactions in a nonlinear quadratic term. We then recast the model inference problem in the form of a dynamic optimal transport framework and solve it efficiently with the adjoint method of optimal control. We evaluated GraphFP on the time series scRNA-seq data set of embryonic murine cerebral cortex development. We illustrated that it 1) reconstructs cell state potential energy, which is a measure of cellular differentiation potency, 2) faithfully charts the probability flows between paired cell states over the dynamic processes of cell differentiation, and 3) accurately quantifies the stochastic dynamics of cell type frequencies on probability simplex in continuous time. We also illustrated that GraphFP is robust in terms of cluster labelling with different resolutions, as well as parameter choices. Meanwhile, GraphFP provides a model-based approach to delineate the cell-cell interactions that drive cell differentiation. GraphFP software is available at https://github.com/QiJiang-QJ/GraphFP.

Dynamic inference of cell development processes from time series scRNA-seq data is a major challenge. Here, we present GraphFP, a coherent computational framework that simultaneously reconstructs the cell state-transition complex potential energy landscape and infers cell-cell interactions from time series single-cell transcriptomic data. Based on the mathematical framework of nonlinear Fokker-Planck equation on graph, GraphFP models the stochastic dynamics of the cell state/type frequencies on probability simplex in continuous time, where the free energy with a nonlinear quadratic interaction term is employed to characterize cell-cell interactions. We formulate the model inference problem in the form of a dynamic optimal transport framework and solve it efficiently with the celebrated adjoint method. GraphFP allows for 1) reconstructing cell state potential energy, which is a measure of cellular differentiation potency, 2) charting the probability flows between paired cell states over dynamic processes, 3) quantifying the stochastic dynamics of cell type frequencies on probability simplex in continuous time, and 4) delineating cell-cell interactions that drive cell differentiation. We show how GraphFP can be used to faithfully reveal and accurately quantify the cell development processes using the embryonic murine cerebral cortex development time series scRNA-seq dataset.