About

Alexander Lee Chang is an Assistant Professor of Adult Psychiatry at the University of Chicago. He is part of the Biological Sciences Division and is affiliated with UChicago Medicine and the Pritzker School of Medicine. His professional profile indicates a focus on research within the field of psychiatry, although specific details about his research focus, background, and key contributions are not provided on the page. For further information, contact can be made via his email alchang@uchicago.edu.

Research topics

• Internal medicine
• Medicine
• Biology
• Immunology
• Pathology
• Cell biology
• Cancer research
• Oncology
• Urology
• Endocrinology
• Computational biology
• Intensive care medicine
• Genetics

Selected publications

• The Banff 2024 Kidney Meeting Report: Rejection as a Spectrum of Phenotypes and Focus on Non-Rejection Glomerular Disease

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  preprintOpen access
  PublisherDOI

• Immune cell quantification of in situ inflammation partitions human lupus nephritis into mechanistic subtypes

  Journal of Clinical Investigation · 2025-09-04 · 5 citations

  articleOpen access

  BACKGROUNDIn human lupus nephritis (LuN), tubulointerstitial inflammation (TII) is prognostically more important than glomerular inflammation. However, a comprehensive understanding of both TII complexity and heterogeneity is lacking.METHODSHerein, we used high-dimensional confocal microscopy, spatial transcriptomics, and specialized computer vision techniques to quantify immune cell populations and localize these within normal and diseased renal cortex structures. With these tools, we compared LuN to renal allograft rejection (RAR) and normal kidney tissues on 54 deidentified biopsies.RESULTSIn both LuN and RAR, the 33 characterized immune cell populations formed discrete subgroups whose constituents covaried in prevalence across biopsies. In both diseases, these covariant immune cell subgroups organized into the same unique niches. Therefore, inflammation could be resolved into trajectories representing the relative prevalence and density of cardinal immune cell members of each covariant subgroup. Indeed, in any one biopsy, the inflammatory state could be characterized by quantifying constituent immune cell trajectories. Remarkably, LuN heterogeneity could be captured by quantifying a few myeloid immune cell trajectories, while RAR was more complex with additional T cell trajectories.CONCLUSIONSOur studies identify rules governing renal inflammation and thus provide an approach for resolving LuN into discrete mechanistic categories.FUNDINGNIH (U19 AI 082724 [MRC], R01 AI148705 [MRC and ASC]), Chan Zuckerberg Biohub (MRC), and Lupus Research Alliance (MRC).

  PublisherOA PDFDOI

• Voclosporin Nephrotoxicity: A Myth Debunked?

  Arthritis & Rheumatology · 2025-05-02

  editorial1st authorCorresponding

  Disclosure Form Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.

  PublisherDOI

• 11 Adult Asthma at Autopsy: A Retrospective Autopsy Review

  Laboratory Investigation · 2025-03-01

  articleOpen access
  PublisherOA PDFDOI

• Renal Whodunit: The Paraprotein Connection

  Journal of the American Society of Nephrology · 2024-10-01 · 1 citations

  articleSenior author
  PublisherDOI

• Thrombotic Microangiopathies and the Kidney

  Advances in Kidney Disease and Health · 2024-05-01 · 5 citations

  reviewSenior author
  PublisherDOI

• Renal cell classification in highly multiplexed microscopy imaging using a biology-based decision tree classifier

  2024-04-02 · 1 citations

  article

  Single-cell sequencing and proteomics have been critical for the study of human disease. However, highly multiplexed microscopy has revolutionized spatial biology by measuring cell expression from ~50 proteins while maintaining spatial locations of cells. This presents unique computational challenges; acquiring manual annotations across so many image channels is challenging, therefore supervised learning methods for classification are undesirable. To overcome this limitation we have developed a decision-tree classifier for the multiclass annotation of renal cells that is analogous to well-established flow cytometry-based cell analyses. We demonstrate this method of cell annotation in a dataset of 54 kidney biopsies from patients with three different pathologies: 25 with lupus nephritis, 23 with renal allograft rejection, and six with non-autoimmune conditions. Biopsies were iteratively stained and imaged using the PhenoCycler protocol to acquire high-resolution, full-section images with a 43-marker panel. Nucleus segmentation was performed using Cellpose2.0 and whole cell segmentation was approximated by dilating the nucleus masks. In our decision tree, cells are sequentially sorted into marker-negative and marker-positive populations using their mean fluorescence intensity (MFI). A multi-Otsu threshold, in conjunction with manual spot checking, is used for determining the optimal MFI threshold for each branching of the decision tree. Marker order is based upon well-established, hierarchical expression of immunological cell markers created in consultation with expert immunologists. We have further developed another algorithm to probe microtubule organizing center polarization, an important immunologic behavior. Ultimately, we were able to assign biologically-defined cell classes to 1.59 million of 2.19 million cells captured in tissue.

  PublisherDOI

• Multi-class instance segmentation of renal structures in highly multiplexed immunofluorescence microscopy images

  2024-04-02

  article

  Lupus nephritis (LN) is a severe manifestation of systemic lupus erythematosus, with up to 30% of LN patients progressing to end-stage kidney disease within ten years of diagnosis. Spatial relationships between specific types of immune cells and kidney structures hold valuable information clinically and biologically. Thus, we develop a modular computational pipeline to analyze the spatially resolved molecular features from high-plex immunofluorescence imaging data. Here, we present three modules of the pipeline, with the goal of achieving multiclass segmentation of renal cells and structures.

  PublisherDOI

• Pseudo-spectral angle mapping for pixel and cell classification in highly multiplexed immunofluorescence images

  Journal of Medical Imaging · 2024-12-10

  articleOpen access

  PurposeThe rapid development of highly multiplexed microscopy has enabled the study of cells embedded within their native tissue. The rich spatial data provided by these techniques have yielded exciting insights into the spatial features of human disease. However, computational methods for analyzing these high-content images are still emerging; there is a need for more robust and generalizable tools for evaluating the cellular constituents and stroma captured by high-plex imaging. To address this need, we have adapted spectral angle mapping—an algorithm developed for hyperspectral image analysis—to compress the channel dimension of high-plex immunofluorescence (IF) images.ApproachHere, we present pseudo-spectral angle mapping (pSAM), a robust and flexible method for determining the most likely class of each pixel in a high-plex image. The class maps calculated through pSAM yield pixel classifications which can be combined with instance segmentation algorithms to classify individual cells.ResultsIn a dataset of colon biopsies imaged with a 13-plex staining panel, 16 pSAM class maps were computed to generate pixel classifications. Instance segmentations of cells with Cellpose2.0 (F1-score of 0.83±0.13) were combined with these class maps to provide cell class predictions for 13 cell classes. In addition, in a separate unseen dataset of kidney biopsies imaged with a 44-plex staining panel, pSAM plus Cellpose2.0 (F1-score of 0.86±0.11) detected a diverse set of 38 classes of structural and immune cells.ConclusionsIn summary, pSAM is a powerful and generalizable tool for evaluating high-plex IF image data and classifying cells in these high-dimensional images.

  PublisherOA PDFDOI

• Androgens contribute to sex bias of autoimmunity in mice by T cell-intrinsic regulation of Ptpn22 phosphatase expression

  Nature Communications · 2024-09-03 · 5 citations

  articleOpen access

  Autoimmune diseases such as systemic lupus erythematosus (SLE) display a strong female bias. Although sex hormones have been associated with protecting males from autoimmunity, the molecular mechanisms are incompletely understood. Here we report that androgen receptor (AR) expressed in T cells regulates genes involved in T cell activation directly, or indirectly via controlling other transcription factors. T cell-specific deletion of AR in mice leads to T cell activation and enhanced autoimmunity in male mice. Mechanistically, Ptpn22, a phosphatase and negative regulator of T cell receptor signaling, is downregulated in AR-deficient T cells. Moreover, a conserved androgen-response element is found in the regulatory region of Ptpn22 gene, and the mutation of this transcription element in non-obese diabetic mice increases the incidence of spontaneous and inducible diabetes in male mice. Lastly, Ptpn22 deficiency increases the disease severity of male mice in a mouse model of SLE. Our results thus implicate AR-regulated genes such as PTPN22 as potential therapeutic targets for autoimmune diseases. Androgen is a sex hormone that may contribute to sex biases in autoimmunity. Here the authors show that in T cells androgen induces the expression of Ptpn22 phosphatase to negatively regulate T cell activation, thereby contributing to protecting males from major autoimmune diseases such as systemic lupus erythematosus and type 1 diabetes.

  PublisherOA PDFDOI

Frequent coauthors

• Agnes B. Fogo

  123 shared

• Richard J. Quigg

  University at Buffalo, State University of New York

  121 shared

• Scott Henderson

  University of Massachusetts Chan Medical School

  102 shared

• Parmjeet Randhawa

  University of Pittsburgh

  101 shared

• Andre Buchler

  Délégation Paris 6

  100 shared

• Weston Rosenthal

  Délégation Paris 6

  100 shared

• Kumamoto Taizo Hibi

  Délégation Paris 6

  100 shared

• Seong Ho

  Délégation Paris 6

  100 shared

Labs

• Alexander Lee ChangPI

Education

• Renal pathology fellowship, Pathology

  University of Washington Medical Center

  2004

• Anatomic / Clinical Pathology residency, Pathology

  University of Washington Medical Center

  2004

• MD

  Albany Medical College

  1999

• BS

  Massachusetts Institute of Technology

  1995