Welcome to the Deshpande Lab

Developing computational methods to study cancer using single-cell and spatial omics data.

Explore Our Research

About the Lab

Spatial and single-cell cancer analysis

Computational oncology at Johns Hopkins

The Deshpande Lab develops computational methods that connect the biology of cancer to quantitative analysis, using experimental single-cell and spatial omics data to characterize tumors across scales.

  • Latent-space and statistical methods to map molecular interactions in the tumor microenvironment.
  • Integration of spatial transcriptomics, imaging, and single-cell RNA-seq.
  • Multi-scale and quantitative systems pharmacology (QSP) models of tumor–immune dynamics.

The lab is part of the Sidney Kimmel Comprehensive Cancer Center (SKCCC) at the Johns Hopkins University School of Medicine. It is directed by Atul Deshpande, PhD, Assistant Professor of Oncology, and collaborates closely with the Popel, Phillip, Stein‑O'Brien, and Ho groups on spatial and systems-level cancer research.

News

  • 2026 — FunCN published in Cancer Immunology Research. Read
  • Apr 2026 — TRACER preprint posted on bioRxiv. Preprint
  • Apr 2026 — STAPLE preprint posted on bioRxiv. Preprint
  • 2026 — Sharanya Goswami joins the lab as an undergraduate research assistant.

Research

We build and apply quantitative methods to understand how cancer is organized in space and across cell types.

Spatial biology

Uncovering the spatial landscape of molecular interactions within the tumor microenvironment, powered by our SpaceMarkers framework. Learn more →

Single-cell analysis

Single-cell RNA-seq methods to dissect tumor heterogeneity and immune response in cancer immunotherapy. Learn more →

Tumor microenvironment modeling

Multi-scale and quantitative systems pharmacology (QSP) models that link mechanistic biology to predictive, virtual-trial simulations. Learn more →

Multi-scale integration

Joint analysis of spatial, imaging, and single-cell omics to build a multi-scale characterization of cancer. Learn more →

Software & Tools

Open methods and packages developed in the lab. Tools marked in development are part of ongoing projects.

R / Bioconductor

SpaceMarkers

Identifies molecular interactions and signaling between cell types directly from spatial transcriptomics data using latent spaces. SpaceMarkers 2.0 is in active development.

Bioconductor · GitHub · Manuscript

In collaboration with Fertig Lab
R / Bioconductor

scatterHatch

Generates colorblind-friendly scatter plots for single-cell and spatial data by adding distinguishable hatch patterns.

Bioconductor · GitHub · Manuscript

In collaboration with Fertig Lab
In development

TRACER

Reconstructs biologically coherent cellular profiles from imaging-based spatial transcriptomics using gene–gene coherence and transcript co-localization.

Tutorial · Preprint

In development

STAPLE

Automates end-to-end spatial transcriptomics analysis — cell typing, neighborhoods, and cell–cell communication — with an AI layer that synthesizes results into interpretable reports.

Preprint

In collaboration with Fertig Lab
Scripts · in development

FunCN

Defines functionally relevant cellular neighborhoods in spatial proteomics by modeling cellular influence through proximity and frequency. Currently a research codebase; packaging in progress.

Manuscript

In collaboration with Ho Lab

Selected Publications

Selected work.

  • Yuan L, Zheng Y, Zhang S, Beroukhim R, Deshpande A. Reconstructing biologically coherent cellular profiles from imaging-based spatial transcriptomics (TRACER). bioRxiv (preprint) (2026). biorxiv.org/10.64898/2026.03.08.710395
  • Lvovs D, Quinn J, Forjaz A, et al., Deshpande A, Noble M, Fertig EJ (co-corresponding). STAPLE: automating spatial transcriptomics analysis and AI interpretation. bioRxiv (preprint) (2026). biorxiv.org/10.64898/2026.03.30.715127
  • Cho Y, Lee JW, Shin SM, et al., Deshpande A, Ho WJ. Computational modeling of cellular influence delineates functionally relevant and distinct cellular neighborhoods in primary and metastatic pancreatic ductal adenocarcinoma (FunCN). Cancer Immunology Research (2026); 14(4):571–584. aacrjournals.org
  • Deshpande A, Loth M, Sidiropoulos DN, et al., Stein-O'Brien G, Kagohara LT, Fertig EJ. Uncovering the spatial landscape of molecular interactions within the tumor microenvironment through latent spaces (SpaceMarkers). Cell Systems (2023). doi:10.1016/j.cels.2023.03.004
  • Guha T, Fertig EJ, Deshpande A. Generating colorblind-friendly scatter plots for single-cell data (scatterHatch). eLife (2022). doi:10.7554/eLife.82128

Full publication list on Google Scholar and PubMed.

People

The people behind the lab's research.

AD

Atul Deshpande, PhD

Principal Investigator · Assistant Professor of Oncology

Directs the lab's research in computational oncology, spatial transcriptomics, and tumor microenvironment modeling at the Sidney Kimmel Comprehensive Cancer Center. Full bio →

OS

Orian Stapleton

PhD student

Develops SpaceMarkers 2.0, extending spatial interaction discovery to new platforms and analysis settings. Bio →

YC

Yeonju Cho

Alumna · Graduate research assistant

Works on imaging mass cytometry (IMC) analysis of the tumor microenvironment; led the FunCN study in pancreatic cancer. Bio →

LY

Long Yuan

PhD student

Leads TRACER and SPHERE-PCA, spatial/single-cell projects (the latter with the Szalay group). Bio →

CC

Chase Christenson

Postdoctoral researcher

Organoid modeling and spatial QSP (spQSP / PhysiCell) for pancreatic cancer. Bio →

SG

Sharanya Goswami

Undergraduate research assistant

Undergraduate research assistant on the lab's spatial biology projects. Bio →

View all people

Contact

Interested in collaborating or joining the lab? Get in touch.

Location:

Sidney Kimmel Comprehensive Cancer Center
Johns Hopkins School of Medicine, Baltimore, MD