Nick Borcherding
Assistant Professor of Pathology & Immunology
Washington University School of Medicine in St. Louis
Integrating systems immunology, single-cell sequencing, and computational frameworks to understand how the adaptive immune system encodes and recalls disease.
01 About
I am a physician-scientist whose work bridges computational immunology, clinical pathology, and transplant immunogenetics.
My clinical practice centers on human leukocyte antigen testing for transplantation, autoimmunity, and cancer immunotherapy. My research asks how the adaptive immune system records disease experience and recalls it later. I read that record using innate and adaptive cellular barcodes, including mitochondrial genomes and immune receptor repertoires, to trace clonal relationships across tissues and disease states.
- Tumor Immunology
- Immunometabolism
- Single-Cell Immune Profiling
- Adaptive Immune Receptor Repertoire Analyses
- Open Data Science
02 Research
Adaptive immune receptor repertoires
T and B cell receptors are natural barcodes. I build methods that read them at single-cell scale to track clonal lineages, measure repertoire diversity, and link receptor sequence to cell state across tumors, transplants, and autoimmune disease.
Single-cell systems immunology
I pair single-cell sequencing with systems-level models to map immune diversity and connect transcriptional programs to function. The goal is a quantitative picture of how immune populations shift across disease and therapy.
Computational frameworks and machine learning
I develop open-source software and deep learning models that turn raw immune sequencing into interpretable structure, from autoencoders that vectorize receptor sequences to enrichment methods that score pathways at single-cell resolution.
Clinical immunogenetics
In the HLA laboratory I work on histocompatibility testing for transplant, autoimmunity, and immunotherapy, and on bringing repertoire and network analyses to bear on real clinical outcomes such as allograft survival.
03 Software
Open-source tools for immune repertoire analysis and single-cell genomics, maintained under BorchLab.
scRepertoire
Single-cell immune receptor profiling that pairs TCR and BCR data with scRNA-seq for clonal quantification, diversity, and repertoire overlap.
immReferent
A clean R interface to IMGT and OGRDB germline references for TCR, BCR, and HLA immunogenomics workflows.
immLynx
A unified R interface that runs Python TCR pipelines such as tcrdist3, OLGA, clusTCR, and ESM-2 embeddings inside scRepertoire workflows.
immGLIPH
An R implementation of GLIPH and GLIPH2 that groups TCRs by CDR3 similarity into specificity clusters likely to share peptide-HLA targets.
immApex
A unified interface for machine learning on adaptive immune receptor sequences, from featurization to model benchmarking.
Trex
Deep learning autoencoders that vectorize TCR CDR3 sequences for receptor-based dimensional reduction.
Ibex
The BCR counterpart to Trex that vectorizes immunoglobulin CDR3 sequences for repertoire-based clustering.
escape
Single-cell gene set enrichment analysis integrated with Seurat and SingleCellExperiment objects.
bHIVE
B-cell Hybrid Immune Variant Engine, a modular artificial immune system for clustering and classification inspired by somatic hypermutation and germinal-center selection.
04 Selected publications
scRepertoire 2: Enhanced and efficient toolkit for single-cell immune profiling
PLoS computational biology 2025; 21(6):e1012760
Abstract
Single-cell adaptive immune receptor repertoire sequencing (scAIRR-seq) and single-cell RNA sequencing (scRNA-seq) provide a transformative approach to profiling immune responses at unprecedented resolution across diverse pathophysiologic contexts. This work presents scRepertoire 2, a substantial update to our R package for analyzing and visualizing single-cell immune receptor data. This new version introduces an array of features designed to enhance both the depth and breadth of immune receptor analysis, including improved workflows for clonotype tracking, repertoire diversity metrics, and novel visualization modules that facilitate longitudinal and comparative studies. Additionally, scRepertoire 2 offers seamless integration with contemporary single-cell analysis frameworks like Seurat and SingleCellExperiment, allowing users to conduct end-to-end single-cell immune profiling with transcriptomic data. Performance optimizations in scRepertoire 2 resulted in a 85.1% increase in speed and a 91.9% reduction in memory usage from the first version over the range repertoire size tested in benchmarking, addressing the demands of the ever-increasing size and scale of single-cell studies. This release marks an advancement in single cell immunogenomics, equipping researchers with a robust toolset to uncover immune dynamics in health and disease.
CD4+ T cells exhibit distinct transcriptional phenotypes in the lymph nodes and blood following mRNA vaccination in humans
Nature immunology 2024; 25(9):1731-1741
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and mRNA vaccination induce robust CD4+ T cell responses. Using single-cell transcriptomics, here, we evaluated CD4+ T cells specific for the SARS-CoV-2 spike protein in the blood and draining lymph nodes (dLNs) of individuals 3 months and 6 months after vaccination with the BNT162b2 mRNA vaccine. We analyzed 1,277 spike-specific CD4+ T cells, including 238 defined using Trex, a deep learning-based reverse epitope mapping method to predict antigen specificity. Human dLN spike-specific CD4+ follicular helper T (TFH) cells exhibited heterogeneous phenotypes, including germinal center CD4+ TFH cells and CD4+IL-10+ TFH cells. Analysis of an independent cohort of SARS-CoV-2-infected individuals 3 months and 6 months after infection found spike-specific CD4+ T cell profiles in blood that were distinct from those detected in blood 3 months and 6 months after BNT162b2 vaccination. Our findings provide an atlas of human spike-specific CD4+ T cell transcriptional phenotypes in the dLNs and blood following SARS-CoV-2 vaccination or infection.
The power and potential of mitochondria transfer
Nature 2023; 623(7986):283-291
Abstract
Mitochondria are believed to have originated through an ancient endosymbiotic process in which proteobacteria were captured and co-opted for energy production and cellular metabolism. Mitochondria segregate during cell division and differentiation, with vertical inheritance of mitochondria and the mitochondrial DNA genome from parent to daughter cells. However, an emerging body of literature indicates that some cell types export their mitochondria for delivery to developmentally unrelated cell types, a process called intercellular mitochondria transfer. In this Review, we describe the mechanisms by which mitochondria are transferred between cells and discuss how intercellular mitochondria transfer regulates the physiology and function of various organ systems in health and disease. In particular, we discuss the role of mitochondria transfer in regulating cellular metabolism, cancer, the immune system, maintenance of tissue homeostasis, mitochondrial quality control, wound healing and adipose tissue function. We also highlight the potential of targeting intercellular mitochondria transfer as a therapeutic strategy to treat human diseases and augment cellular therapies.
Dietary lipids inhibit mitochondria transfer to macrophages to divert adipocyte-derived mitochondria into the blood
Cell metabolism 2022; 34(10):1499-1513.e8
Abstract
Adipocytes transfer mitochondria to macrophages in white and brown adipose tissues to maintain metabolic homeostasis. In obesity, adipocyte-to-macrophage mitochondria transfer is impaired, and instead, adipocytes release mitochondria into the blood to induce a protective antioxidant response in the heart. We found that adipocyte-to-macrophage mitochondria transfer in white adipose tissue is inhibited in murine obesity elicited by a lard-based high-fat diet, but not a hydrogenated-coconut-oil-based high-fat diet, aging, or a corn-starch diet. The long-chain fatty acids enriched in lard suppress mitochondria capture by macrophages, diverting adipocyte-derived mitochondria into the blood for delivery to other organs, such as the heart. The depletion of macrophages rapidly increased the number of adipocyte-derived mitochondria in the blood. These findings suggest that dietary lipids regulate mitochondria uptake by macrophages locally in white adipose tissue to determine whether adipocyte-derived mitochondria are released into systemic circulation to support the metabolic adaptation of distant organs in response to nutrient stress.
Mapping the immune environment in clear cell renal carcinoma by single-cell genomics
Communications biology 2021; 4(1):122
Abstract
Clear cell renal cell carcinoma (ccRCC) is one of the most immunologically distinct tumor types due to high response rate to immunotherapies, despite low tumor mutational burden. To characterize the tumor immune microenvironment of ccRCC, we applied single-cell-RNA sequencing (SCRS) along with T-cell-receptor (TCR) sequencing to map the transcriptomic heterogeneity of 25,688 individual CD45+ lymphoid and myeloid cells in matched tumor and blood from three patients with ccRCC. We also included 11,367 immune cells from four other individuals derived from the kidney and peripheral blood to facilitate the identification and assessment of ccRCC-specific differences. There is an overall increase in CD8+ T-cell and macrophage populations in tumor-infiltrated immune cells compared to normal renal tissue. We further demonstrate the divergent cell transcriptional states for tumor-infiltrating CD8+ T cells and identify a MKI67 + proliferative subpopulation being a potential culprit for the progression of ccRCC. Using the SCRS gene expression, we found preferential prediction of clinical outcomes and pathological diseases by subcluster assignment. With further characterization and functional validation, our findings may reveal certain subpopulations of immune cells amenable to therapeutic intervention.
scRepertoire: An R-based toolkit for single-cell immune receptor analysis
F1000Research 2020; 9:47
Abstract
Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine mRNA and immune profiling, scRepertoire was built to process data derived from 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with a number of popular R packages for single-cell expression, such as Seurat. The scRepertoire R package and processed data are open source and available on GitHub and provides in-depth tutorials on the capability of the package.
Single-Cell Profiling of Cutaneous T-Cell Lymphoma Reveals Underlying Heterogeneity Associated with Disease Progression
Clinical cancer research : an official journal of the American Association for Cancer Research 2019; 25(10):2996-3005
Abstract
PURPOSE: Cutaneous T-cell lymphomas (CTCL), encompassing a spectrum of T-cell lymphoproliferative disorders involving the skin, have collectively increased in incidence over the last 40 years. Sézary syndrome is an aggressive form of CTCL characterized by significant presence of malignant cells in both the blood and skin. The guarded prognosis for Sézary syndrome reflects a lack of reliably effective therapy, due, in part, to an incomplete understanding of disease pathogenesis. EXPERIMENTAL DESIGN: Using single-cell sequencing of RNA and the machine-learning reverse graph embedding approach in the Monocle package, we defined a model featuring distinct transcriptomic states within Sézary syndrome. Gene expression used to differentiate the unique transcriptional states were further used to develop a boosted tree classification for early versus late CTCL disease. RESULTS: Our analysis showed the involvement of FOXP3 + malignant T cells during clonal evolution, transitioning from FOXP3 + T cells to GATA3 + or IKZF2 + (HELIOS) tumor cells. Transcriptomic diversities in a clonal tumor can be used to predict disease stage, and we were able to characterize a gene signature that predicts disease stage with close to 80% accuracy. FOXP3 was found to be the most important factor to predict early disease in CTCL, along with another 19 genes used to predict CTCL stage. CONCLUSIONS: This work offers insight into the heterogeneity of Sézary syndrome, providing better understanding of the transcriptomic diversities within a clonal tumor. This transcriptional heterogeneity can predict tumor stage and thereby offer guidance for therapy.
05 Contact
- ncborch@gmail.com
- Location
- St. Louis, MO
- Institution
- Washington University School of Medicine in St. Louis
- Elsewhere
See my disclosures.