flow cytometry

Multiplex single-cell chemical genomics to identify small molecule modulators of tumor cell-intrinsic immunogenicity in glioblastoma

PROJECT SUMMARY/ABSTRACT Glioblastoma multiforme is the most common and aggressive primary brain cancer. Despite a multimodal treatment regimen of surgical resection, chemotherapy, radiotherapy, and tumor-treating fields, most patients succumb to the disease within two years of diagnosis. Cancer immunotherapy strategies have emerged as a powerful tool for treating aggressive solid tumors such as melanoma and non-small cell lung cancer. However, current strategies have led to low response rates in glioblastoma, resulting from its low immunogenicity. The proposed research program aims to identify small molecules capable of increasing the immunogenicity of glioblastoma cells, focusing on altering gene expression programs associated with recognition by the immune system and the ability of cytotoxic immune cells to target glioblastoma for destruction. We will use highly multiplex chemical transcriptomic profiling to determine the molecular consequence of exposing glioblastoma neurosphere models to 3,792 small molecules, targeting the majority of cellular activities and clinically relevant drug targets as well as a collection of previously identified immunomodulators. We will then determine how each exposure alters the expression of gene programs associated with tumor cell immunogenicity and response to therapy, including the expression of genes associated with the recognition by the immune system and those associated with immune checkpoints, as well as programs more broadly correlated with resistance to anti-cancer therapies. Chemical hits that meet specific criteria will be subjected to a medicinal chemistry review to further classify compounds by their suitability for treating malignancies in the brain. We will then screen chemical hits to determine their ability to modulate immune-mediated tumor cell killing using tumor- immune cell co-culture. Lastly, we will leverage gene editing and flow cytometry to validate hits based on on- target molecular effects and further refine the mechanism of action by inspecting the ability of drugs to modulate immunogenic programs at the protein level. Our chemical genomics screens aim to provide crucial information regarding the link between pathway activity and immunomodulation in GBM, a critical step to guide future efforts in GBM immunotherapy. More broadly, our study will establish single-cell chemical genomics as a scalable platform for phenotype-based screening for preclinical prioritization of chemical modulators of complex transcriptional phenotypes and provide a framework for hit prioritization, establishment of pipeline robustness and hit validation in the context of single- cell chemical genomics screens.

Overcoming Treatment Resistance by Targeting Polyploid Breast Cancer Cells with AI assisted Single-Cell Analysis

Therapy resistance remains a formidable challenge in breast cancer treatment, with emerging evidence identifying polyploid giant cancer cells (PGCCs) as key drivers. These cells, arising through whole-genome doubling (WGD) events, exhibit enhanced resistance to therapies, contributing to disease relapse. PGCCs are characterized by enlarged cell and nuclear sizes, increased DNA content, and greater resilience compared to non-PGCCs. Their prevalence escalates with disease progression and therapeutic stress, underscoring their critical role in treatment resistance. As such, we hypothesize that inhibiting polyploid cancer cells can effectively reduce therapeutic resistance. Despite this, effective strategies targeting PGCCs are limited, hindered by the lack of high-throughput methods to assess PGCC viability and abundance. Traditional screening assays lack the sensitivity to detect the elimination of small populations of PGCCs, while current detection methods, such as visual inspection and flow cytometry, are not suited for high-throughput compound screening. Our preliminary work has established a high-throughput single-cell morphological analysis pipeline capable of quantifying PGCCs, and we successfully screened 2,726 compounds for their efficacy on PGCCs. Based on the preliminary success, we aim to further improve its robustness and accuracy under diverse staining and imaging conditions, ensuring consistent performance across multiple labs for widespread use in PGCC/WGD studies, with deep learning to accelerate the discovery of therapeutic strategies targeting PGCCs. In addition to empirical screening, our scRNA-Seq analysis of PGCCs has revealed altered gene expression, particularly in genes associated with FOXM1, a transcription factor critical in cell cycle regulation and linked to poor outcomes in various cancers. PGCCs also show altered ferroptosis regulators and elevated reactive oxygen species (ROS), indicating susceptibility to ferroptosis. Here, we propose two independent and complementary aims. Aim 1: We will develop and validate a robust deep learning–based single-cell morphological analysis pipeline for accurate PGCC/non-PGCC discrimination across variable staining, imaging, and lab settings. The model will be benchmarked on independent datasets from external labs and released as open-source, version-controlled software with full documentation to support reproducibility and broad adoption in PGCC/WGD research. Aim 2: Leveraging our screen of 2,726 FDA-approved compounds and mechanistic studies of FOXM1 and ferroptosis, we will prioritize and validate therapies that eradicate PGCCs and reduce treatment resistance. Using patient- derived cells, 3D spheroids, and syngeneic/xenograft models, we will rigorously assess top candidates as monotherapy and in combination with standard-of-care agents. Successful completion of this project will accelerate PGCC/WGD research, advance therapeutic strategies to overcome breast cancer resistance, and especially deliver benefits to patients with high PGCC burden. Given the prevalence of WGD across solid tumors and its induction by standard therapies, our approach holds broad clinical relevance and translational impact.

Autoreactive T cells in lupus

The autoimmune disease systemic lupus erythematosus (SLE) is characterized by loss of adaptive immune tolerance in conjunction with innate immune system hyperactivity. Autoantibodies, produced by plasma cells derived from activated B cells, form proinflammatory immune complexes. These immune complexes drive feed forward loops that sustain a systemic inflammatory environment and deposit in tissues leading to potentially fatal organ damage. B cells receive help from T cells to produce antibodies. They also contribute to disease by shaping T cell responses and secreting cytokines. Recent case reports in which SLE patients were treated with anti-CD19 CAR-T cell therapy to deplete B cells highlight the pathogenic role of B cells in lupus and their value as a therapeutic target. However, a better understanding of how autoreactive B cells interact with autoreactive T cells may reveal more targeted points of therapeutic intervention that specifically block autoreactive responses while sparing protective ones. Antigen specific interactions between CD4+ T cells and B cells are required for the development of autoimmune disease in lupus. However, whether these critical interactions occur in germinal centers, where competition for CD4+ T cell help selects high affinity B cells, or in extrafollicular responses, where B cells may avoid peripheral tolerance checkpoints, is unclear. Gene expression profiles and pathways specific to autoreactive CD4+ T cells, and how they are shaped by their interaction with autoreactive B cells, are also ill defined. CD8+ T cells, which recognize antigen presented on MHC Class I, have also been suggested to modulate the fate of autoreactive B cells. They can directly kill autoreactive B cells as a means of tolerance, and a subset of CD8+ T cells has recently been shown to have B cell helper function. Whether and how such interactions between B and CD8+ T cells enhance or suppress the development of lupus is unknown. Here, we will use genetic and in vivo proximity labeling approaches to address these knowledge gaps. In Aim 1, we will test the hypothesis that antigen specific interactions between B and CD8+ T cells promote B cell activation and autoantibody production in lupus. We will prevent B cells, but not other cells, from undergoing cognate interactions with CD8+ T cells via B cell-specific deletion of B2M, a component of the MHC Class I complex, in two lupus models. In Aim 2, will use the uLIPSTIC in vivo proximity system to label all T cells interacting with B cells in lupus models compared to wild type controls. Features specific to these autoreactive T cells will be defined by flow cytometry, scRNA Seq, and scTCR-Seq. These studies will provide valuable molecular and cellular insight into the mutual activation of B and T cells in lupus. They will set the stage for future mechanistic studies defining the role of autoreactive T cell specific genes and pathways and potentially highlight new therapeutic targets specific to autoreactive B/T interactions.

Gene-free landscape models for development

Fate decisions in developing tissues involve cells transitioning between a set of discrete cell states. Geometric models, often referred to as Waddington landscapes, are an appealing way to describe differentiation dynamics and developmental decisions. We consider the differentiation of neural and mesodermal cells from pluripotent mouse embryonic stem cells exposed to different combinations and durations of signalling factors. We developed a principled statistical approach using flow cytometry data to quantify differentiating cell states. Then, using a framework based on Catastrophe Theory and approximate Bayesian computation, we constructed the corresponding dynamical landscape. The result was a quantitative model that accurately predicted the proportions of neural and mesodermal cells differentiating in response to specific signalling regimes. Taken together, the approach we describe is broadly applicable for the quantitative analysis of differentiation dynamics and for determining the logic of developmental cell fate decisions.

Neonatal microglia phenotyping by flow cytometry: impact of inflammation

Investigating gut-microbe interactions and epithelial α-synuclein through human enteroid monolayers and imaging flow cytometry of enteroendocrine cells in vitro

FENS Forum 2024