About

Dr. Jeffrey Moffitt is an Associate Professor at Boston Children's Hospital and Harvard Medical School. His research focuses on the development and application of advanced imaging and molecular techniques to understand cellular and molecular mechanisms. His work involves exploring the intricacies of biological systems through innovative approaches, contributing significantly to the fields of cellular and molecular biology.

Research topics

• Computer Science
• Biology
• Computational biology
• Genetics
• Artificial Intelligence
• Anatomy
• Cell biology
• Bioinformatics
• Computer vision
• Cancer research
• Immunology
• Data science

Selected publications

• Barrier Immune Memory is Promoted by Intestinal Epithelial Cell Presentation of Injected Bacterial Antigens

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-31

  articleOpen access

  Abstract The contributions of antigen compartmentalization to recognition differences between CD4 and CD8 T cells have long been appreciated, but little is known of how subcellular localization of different antigens expressed by a single pathogen impacts T cell immunity. By tracking a clonal CD4 T cell response to its cognate epitope shuttled between different virulence proteins of the enteropathogenic bacterium, Citrobacter rodentium ( Cr ), we find a remarkable bias in the magnitude and quality of the response contingent on whether antigen remains bacterially associated or is introduced into intestinal epithelial cells colonized by the bacterium. Only proteins injected into the cytosol of colonocytes via the type 3 secretion system (T3SS) of Cr were found to recruit robust antigen-specific T cell responses to the infected mucosa and give rise to CD4 resident memory T (Trm) cells that populate the mucosal epithelium—and this required direct presentation of these antigens by infected epithelial cells. Single-cell transcriptomic analyses revealed that sustained, bidirectional epithelial-T cell communication was required both to elicit epithelial barrier-protective T cell help and to promote transcriptional networks that program a tissue-residency rather than central memory fate. These results establish a central role for antigen presentation by non-professional APCs in controlling memory fate decisions by CD4 T cells, with important implications for development of successful mucosal vaccines.

  PublisherDOI

• An image-based transcriptomics atlas reveals the regional and microbiota-dependent molecular, cellular, and spatial structure of the murine gut

  Cell Host & Microbe · 2026-02-19 · 1 citations

  articleOpen accessSenior author
  PublisherOA PDFDOI

• Brain-immune interactions generate pathogen-specific sickness states

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-12-08

  articleOpen access

  Abstract In nature, animals encounter diverse pathogens that trigger specific peripheral defense programs and elicit sickness behavior, a set of stereotyped physiological and behavioral changes thought to promote host fitness. Most studies to date have relied on one or a few mouse models of infection, limiting insights into pathogen-specific neuroimmune interactions that generate sickness. We hypothesized that different pathogens might elicit distinct sickness states by engaging different cell types and brain circuits. Using inflammatory models representing bacterial, viral, allergic, parasitic or colitis conditions, we assessed sickness across scales: organismal – behavior and physiology; cellular – brain-wide neural activity; and molecular – single-cell in situ transcriptomics in hypothalamus areas associated with social and homeostatic functions affected during sickness. Remarkably, immune challenges elicited unique repertoires of changes across all scales. Our findings reveal pathogen-specific sickness states encoded by the brain across scales, thereby broadening our understanding of how infections make us sick.

  PublisherDOI

• 1036: THE BIOGEOGRAPHY OF INFLAMMATION ASSOCIATED FIBROBLASTS IN THE HUMAN AND MOUSE INFLAMED GUT

  Gastroenterology · 2025-05-01

  articleSenior author
  PublisherDOI

• Inflammatory and anti-inflammatory cytokines bidirectionally modulate amygdala circuits regulating anxiety

  Cell · 2025-04-01 · 59 citations

  articleOpen access
  PublisherOA PDFDOI

• A Reference Atlas of the Human Dorsal Root Ganglion

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-11-06 · 14 citations

  preprintOpen access

  Somatosensory perception largely emerges from diverse peripheral sensory neurons whose cell bodies reside in dorsal root ganglia (DRG). Damage or dysfunction of DRG neurons is a major cause of chronic pain and sensory loss. In mice, deep single-cell transcriptomic profiling and genetically defined models have offered important clues into DRG function, but in humans, the cellular and molecular landscape of DRG neurons remains less understood. Here, we constructed a reference cell atlas of the human DRG by profiling transcriptomes of cells and nuclei from 126 donors sampled across cervical, thoracic, and lumbar DRGs. This atlas resolves 22 neuronal subtypes, including known and previously unrecognized subtypes linked to nociception, mechanosensation, thermosensation, and proprioception, as well as 10 types of non-neuronal cells. Cross-species integration, spatial transcriptomics, and microneurography enabled cell-type-specific comparisons of soma size and conduction velocity between species. Human DRG somata are larger across all cell types than their mouse counterparts, and the conduction velocities of human hair follicle innervating A-fibers are faster than in mice, suggesting a functional shift in rapid mechanical detection in humans. This integrated human DRG reference cell atlas provides a resource for exploring new molecular and physiological features of human DRG, which could help identify new strategies for treating chronic pain and other diseases of the peripheral nervous system.

  PublisherOA PDFDOI

• Imaging the Intestinal Transcriptome With Multiplexed Error‐Robust Fluorescence In Situ Hybridization (MERFISH)

  Current Protocols · 2025-03-01 · 8 citations

  articleOpen accessSenior authorCorresponding

  Multiplexed error-robust fluorescence in situ hybridization (MERFISH) is a massively multiplexed single RNA-molecule imaging technique capable of spatially resolved single-cell transcriptomic profiling of thousands of genes in millions of cells within intact tissue slices. Initially introduced for brain tissues, MERFISH has since been extended to other tissues, where rapid RNA degradation during the preparation process can pose challenges. This protocol outlines the application of MERFISH in one such challenging tissue, the mammalian gastrointestinal tract. We describe two complementary protocols leveraging either fresh frozen or fixed frozen approaches and describe methods for combining RNA imaging with immunofluorescence. While these protocols were designed and validated in gut tissues, we anticipate that they will be useful resources for the application to other challenging tissue types. © 2025 Wiley Periodicals LLC. Basic Protocol 1: Fixed-frozen sample preparation Basic Protocol 2: Fresh-frozen sample preparation Basic Protocol 3: Encoding probe construction Basic Protocol 4: MERFISH imaging Basic Protocol 5: Image decoding Support Protocol 1: Coverslip silanization Support Protocol 2: Poly-d-lysine (PDL) coating of the coverslips Support Protocol 3: Hybridization buffer preparation Support Protocol 4: Trolox quinone stock preparation Support Protocol 5: TCEP stock preparation Alternate Protocol 1: MERFISH-compatible immunofluorescent boundary stains in fresh frozen tissue Alternate Protocol 2: Immunofluorescent boundary stains with methacrylate-NHS-anchored antibodies for PFA-fixed samples Alternate Protocol 3: Guanidine-HCl tissue clearing.

  PublisherOA PDFDOI

• Protocol optimization improves the performance of multiplexed RNA imaging

  Scientific Reports · 2025-08-31 · 2 citations

  articleOpen accessSenior authorCorresponding

  Spatial transcriptomics has emerged as a powerful tool to define the cellular structure of diverse tissues. One such method is multiplexed error robust fluorescence in situ hybridization (MERFISH). MERFISH identifies RNAs with error tolerant optical barcodes generated through sequential rounds of single-molecule fluorescence in situ hybridization (smFISH). MERFISH performance depends on a variety of protocol choices, yet their effect on performance has yet to be systematically examined. Here we explore a variety of properties to identify optimal choices for probe design, hybridization, buffer storage, and buffer composition. In each case, we introduce protocol modifications that can improve performance, and we show that, collectively, these modified protocols can improve MERFISH quality in both cell culture and tissue samples. As RNA FISH-based methods are used in many different contexts, we anticipate that the optimization experiments we present here may provide empirical design guidance for a broad range of methods.

  PublisherOA PDFDOI

• Protocol Optimization Improves the Performance of Multiplexed RNA Imaging

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-22

  preprintOpen accessSenior authorCorresponding

  Abstract Spatial transcriptomics has emerged as a powerful tool to define the cellular structure of diverse tissues. One such method is multiplexed error robust fluorescence in situ hybridization (MERFISH). MERFISH identifies RNAs with error tolerant optical barcodes generated through sequential rounds of single-molecule fluorescence in situ hybridization (smFISH). MERFISH performance depends on a variety of protocol choices, yet their effect on performance has yet to be systematically examined. Here we explore a variety of properties to identify optimal choices for probe design, hybridization, buffer storage, and buffer composition. In each case, we introduce protocol modifications that can improve performance, and we show that, collectively, these modified protocols can improve MERFISH quality in both cell culture and tissue samples. As RNA FISH-based methods are used in many different contexts, we anticipate that the optimization experiments we present here may provide empirical design guidance for a broad range of methods.

  PublisherOA PDFDOI

• MERFISH data from the murine hypothalamus in health and under induced sickness states

  Zenodo (CERN European Organization for Nuclear Research) · 2025-12-21

  datasetOpen access1st authorCorresponding

  In natural environments, animals encounter diverse pathogens that engage specific peripheral host defense programs and elicit sickness behavior – a set of stereotyped physiological and behavioral changes thought to promote host fitness. So far, most studies have relied on one or few mouse models of infection, limiting insights into pathogen-specific neuroimmune interactions that generate sickness. We hypothesized that different pathogens may elicit distinct sickness states and engage various brain circuits. Using different models of infection and inflammation representing bacterial, viral, allergic, parasitic and colitis conditions, we assessed sickness across scales: organismal – behavior and physiology; cellular – brain-wide neural activity; and molecular – single-cell in situ transcriptomics in the hypothalamus, associated with social and homeostatic functions affected during sickness. Remarkably, immune challenges each elicited unique repertoires of changes across all scales. Our findings reveal specific pathogen-specific sickness states encoded by the brain across scales, thereby broadening our understanding of how infections make us sick. This repository contains MERFISH-related data associated with these studies. Namely it contains the merfish_adata.h5ad file. This file was created from the scanpy package and can be loaded with tools from that package. It contains the following items: * X: The matrix of expression for all cells and all genes. This matrix was divided by total counts per cell then log1p-transformed.* obs: A dataframe with the following properties for all cells The index of each row in this data frame is a unique name for each cell * batch: A unique name for each MERFISH dataset * x: The centroid of that cell in x, in microns * y: The centroid of that cell in y, in microns * z: The average position of that cell in z-stacks, in microns * cellType: The cell type label associated with that cell * condition: The treatment condition for the mouse in which the cell was imaged * section: An index for the section in which the cell was imaged * ignoreCell: A flag that indicates that the cell was not included in subsequent analysis

  PublisherDOI

Frequent coauthors

• Xiaowei Zhuang

  Harvard University Press

  75 shared

• Carlos Bustamante

  University of California, Berkeley

  55 shared

• Rosalind J. Xu

  Boston Children's Museum

  54 shared

• Brianna R. Watson

  Harvard University

  37 shared

• Roni Nowarski

  Brigham and Women's Hospital

  36 shared

• Francisco J. Quintana

  Harvard University

  29 shared

• Ana C. Anderson

  Brigham and Women's Hospital

  28 shared

• Kisha N. Sivanathan

  Brigham and Women's Hospital

  28 shared

Labs

• Moffitt LaboratoryPI

  Not provided