Gregory Warren Schwartz

Tracking transcriptomic profiles of cells over time in response to developmental cues and environmental stimuli can reveal critical insights into the fundamental mechanisms of development and disease. However, longitudinal molecular profiling at the global transcriptome level remains a major challenge, as RNA sequencing fundamentally alters or destroys cells. To overcome these limitations, we developed PENNE, a deep-learning framework that infers whole-transcriptomic profiles directly from live-cell images. Using gated attention mechanisms, PENNE trains on spatial transcriptomic datasets to align morphological features with gene expression. To enable inferences from images, our model performs domain adaptation to eliminate discrepancies between stained and unstained tissue images, effectively transferring molecular information from tissue sections to live-cell imaging. PENNE accurately identifies cell-type-specific and radiation-response markers via imputed expression. Furthermore, using only live-cell images stained with a G2/M cell cycle marker, our model captures temporal gene dynamics, evidenced by strong correlations between predicted expression and both ground-truth cellular confluency and cell-cycle progression. By bridging the gap between data-rich spatial transcriptomics and the practicality of live-cell imaging, PENNE provides a powerful new framework for monitoring molecular temporal dynamics directly through morphological information. This approach enables a paradigm-shifting workflow, fusing transcriptome-wide data with live-cell microscopy to fuel the discovery of novel gene programs via scalable, non-invasive, real-time interrogation of cellular states.

Inflammation activates blood cells, contributing to ageing and malignancy ^1–3 . Haematopoietic stem cells (HSCs) survive a lifetime of infection to sustain life-long haematopoiesis ^1–9 , but how human HSCs respond and adapt to inflammatory stress is largely unknown. Here, to empirically understand this adaptation, we developed xenograft inflammation–recovery models and performed single-cell multiomics on xenografted human HSCs. Two transcriptionally and epigenetically distinct HSC subsets were identified with one, termed HSC inflammatory memory (HSC-iM), retaining a molecular memory of previous inflammatory treatments. The HSC-iM subset exhibited quiescence and restrained haematopoietic output. Molecularly, the HSC-iM program was enriched in HSCs from adult and paediatric samples across conditions ranging from COVID-19 recovery, sickle cell disease, ageing and clonal haematopoiesis, establishing both the validity of our xenograft models and the physiological relevance of HSC-iM. Clonal haematopoiesis mutations in HSC-iM attenuated the effects of inflammatory stress by promoting HSC activation and differentiation. Moreover, transmission of the pro-inflammatory HSC-iM transcriptional program to differentiated immune progeny was demonstrated in xenograft and physiological settings. Finally, HSC-iM program enrichment in circulating blood cells was associated with a heightened risk score for all-cause mortality in population cohort analyses, underscoring the clinical relevance of this newly identified HSC subset in characterizing heterogeneous health outcomes across a lifetime.

Cancer survivors experience long-term skeletal and hematopoietic complications that limit quality of life following chemotherapy (CTX), yet the mechanisms underlying these defects remain incompletely understood. Using a murine model of doxorubicin (DOX)-based leukemia induction therapy, we show that CTX induces inflammatory remodeling of the bone marrow (BM) niche. DOX treatment resulted in loss of arteriolar vasculature, blockade of mesenchymal stromal cell (MSC) differentiation, trabecular bone loss, and reduced niche capacity to maintain hematopoietic stem cells (HSCs). These defects were accompanied by aberrant immune activation within the BM, marked by increased interferon-γ (IFNγ) production by CD8⁺ T cells. Inhibition of IFNγ signaling partially restored arteriolar vessels and adipogenic differentiation. Moreover, combined IFNγ blockade and deferoxamine mesylate (DFM), which promotes vascular recovery, attenuated chemotherapy-associated skeletal damage. Consistent with these findings, paired BM samples collected at diagnosis and post-CTX from leukemia patients exhibited altered MSC lineage priming, upregulation of inflammatory pathways, and expansion of BM CD8⁺ memory T cells after treatment. Together, these findings implicate IFNγ-driven chronic inflammatory remodeling as a central mechanism of CTX-associated BM niche dysfunction and pinpoints inflammatory signaling as a potential target to preserve BM function and long-term tissue integrity.

The spatial arrangement of cells within a tissue microenvironment shapes their interactions and cell states, which are essential for tissue development, homeostasis, and disease. Spatial -omics technologies can precisely map the location of each cell within complex tissue structures, while also profiling their protein content and transcriptional diversity. Various approaches have been developed to analyze spatial patterns of cell aggregation, repulsion, or random distribution within tissues. However, differences in cell morphology within a tissue can introduce significant bias. Cell size in particular is not accounted for and introduces challenges when quantifying the aggregation of cells or their molecular features. To overcome such limitations, we present ClumPyCells: a statistical framework that measures cell and marker aggregation within tissue while correcting for size morphology. ClumPyCells enables interpretation of cell aggregation, bypassing interfering cell types or tissue regions unrelated to the desired spatial correlation. We demonstrate the capabilities of ClumPyCells across several tumor types, including melanoma and colorectal cancer, and spatial -omics technologies such as spatial transcriptomics and proteomics, while benchmarking how cell-size differences contribute to misinterpretations. By correcting for disruptive cell types within a region of interest, ClumPyCells will determine new tissue patterns and structures without morphological interference.

Phase contrast microscopy (PCM) is a powerful cell imaging method, one of the few technologies to delineate and track cell structure in live cells without staining. Despite PCM’s great potential and popularity in monitoring live-cell populations, there is a lack of algorithms to extract morphological information from these images due to the lack of large training datasets. To overcome this challenge and enable advanced, high throughput quantitative analysis of PCM images, we introduce SPAGHETTI: a lightweight image translator built on a modified cycle-consistent generative adversarial network. SPAGHETTI translates PCM images into those resembling hematoxylin and eosin (H&E) pathological images which, due to the pervasive and widespread use in clinical settings, are the basis for most large-scale deep learning models for quantitative analyses. We demonstrate that by first using SPAGHETTI to translate PCM images into H&E-like images, we could achieve significantly improved performance on cell segmentation through the use of tissue and H&E-specific cell segmentation models. We also show that by passing translated PCM images across several independent datasets into H&E feature extractor models, we improve the performance of cell-type annotation, experimental media classification, and cell viability prediction. Overall, SPAGHETTI enables many quantitative analyses of PCM that were previously impossible and acts as a valuable preprocessing step to help researchers gather novel information about cell states through the downstream quantitative analysis of morphological features.

Dysregulation of communication between cells mediates complex diseases such as cancer and diabetes; however, detecting cell–cell communication at scale remains one of the greatest challenges in transcriptomics. Most current single-cell RNA sequencing and spatial transcriptomics computational approaches exhibit high false-positive rates, do not detect signals between individual cells and only identify single ligand–receptor communication. To overcome these challenges, we developed Cell Neural Networks on Spatial Transcriptomics (CellNEST) to decipher patterns of communication. Our model introduces a new type of relay-network communication detection that identifies putative ligand–receptor–ligand–receptor communication. CellNEST detects T cell homing signals in human lymph nodes, identifies aggressive cancer communication in lung adenocarcinoma and colorectal cancer, and predicts new patterns of communication that may act as relay networks in pancreatic cancer. Along with CellNEST, we provide a web-based, interactive visualization method to explore in situ communication. CellNEST is available at https://github.com/schwartzlab-methods/CellNEST.

Summary: The 10x Genomics single cell Multiome (scMultiome) assay enables the simultaneous profiling of chromatin accessibility and gene expression from the same nucleus, and has increasingly been utilized in revealing cellular heterogeneity and gene regulation in cancers. However, a dedicated bioinformatics pipeline specifically designed for this type of data is still lacking. Here we present iSHARC, a streamlined pipeline for quality control, modality integration, clustering, cell type annotation, and regulatory mechanism analysis of individual scMultiome data, as well as for integrating multiple samples. The main advantages of iSHARC are: 1) easy implementation, execution and extension through a modular Snakemake workflow management system; 2) flexible analysis and parameters customization via a single configuration file; and 3) comprehensive accessibility by providing different access points to results and detailed summary reports from a single run. Availability and implementation: This pipeline is an open-source software under the MIT license and it is freely available at https://github.com/yzeng-lol/iSHARC . Contact: yong.zeng@uhn.ca or hansen.he@uhn.ca or mathieu.lupien@uhn.ca Supplementary information: Supplementary data are appended.

T-lineage acute lymphoblastic leukemia (ALL) is an aggressive cancer comprising diverse subtypes that are challenging to stratify using conventional immunophenotyping. To gain insights into subset-specific therapeutic vulnerabilities, we performed an integrative multiomics analysis of bone marrow samples from newly diagnosed T cell ALL, early T cell precursor ALL, and T/myeloid mixed phenotype acute leukemia. Leveraging cellular indexing of transcriptomes and epitopes in conjunction with T cell receptor sequencing, we identified a subset of patient samples characterized by activation of inflammatory and stem gene programs. These inflammatory T-lineage samples exhibited distinct biological features compared with other T-lineage ALL samples, including the production of proinflammatory cytokines, prevalence of mutations affecting cytokine signaling and chromatin remodeling, an altered immune microenvironment, and poor treatment responses. Moreover, we found that, although inflammatory T-lineage ALL samples were less sensitive to dexamethasone, they exhibited unique sensitivity to a BCL-2 inhibitor, venetoclax. To facilitate classification of patients with T-lineage ALL, we developed a computational inflammatory gene signature scoring system, which stratified patients and was associated with disease prognosis in three additional patient cohorts. By identifying a high-risk T-lineage ALL subtype on the basis of an inflammatory score, our study provides a framework for targeted therapeutic approaches for these challenging-to-treat cancers.

Recent advances in high-resolution mapping of spatial interactions among regulatory elements support the existence of complex topological assemblies of enhancers and promoters known as enhancer-promoter hubs or cliques. Yet, organization principles of these multi-interacting enhancer-promoter hubs and their potential role in regulating gene expression in cancer remain unclear. Here, we systematically identify enhancer-promoter hubs in breast cancer, lymphoma, and leukemia. We find that highly interacting enhancer-promoter hubs form at key oncogenes and lineage-associated transcription factors potentially promoting oncogenesis of these diverse cancer types. Genomic and optical mapping of interactions among enhancer and promoter elements further show that topological alterations in hubs coincide with transcriptional changes underlying acquired resistance to targeted therapy in T cell leukemia and B cell lymphoma. Together, our findings suggest that enhancer-promoter hubs are dynamic and heterogeneous topological assemblies with the potential to control gene expression circuits promoting oncogenesis and drug resistance.

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs for atlas-scale datasets like Human Pancreas Analysis Program (HPAP), we develop AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX shows the higher performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulates known islet pathobiology and shows differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8⁺ T cells infiltration in islets during type 1 diabetes progression.