Biology subjects

Jackson, H. W.

Publications and source records attributed to Jackson, H. W..

14 recordsLinked to original sources

bioRxiv2026

Multimodal cell communication networks nominate immunotherapies for RCC subgroups with discrete T cell recruitment or expansion

Renal cell carcinoma (RCC) is amongst the most immune-infiltrated solid tumours, but only a small subset of patients achieves durable response to immune checkpoint blockade therapy. Efforts to characterize the immune microenvironment and molecular regulators responsible for treatment responses have explored numerous facets of disease biology using compartmentalized genomics, transcriptomics, and proteomics datasets, yielding many important yet context and data specific insights. Therefore, to provide a more integrated approach to informing future precision medicine strategies, we combined the complementary strengths of multiple technological platforms to profile multi-regional, spatially annotated surgical biospecimens from 65 RCC patients by single-cell RNA sequencing with paired TCR and BCR repertoire analysis, imaging mass cytometry, suspension mass cytometry, spatial transcriptomics and deconvolved bulk RNA sequencing. With this resource dataset, we explored patient subgroups and precision immunotherapy strategies using an integrated analysis of transcripts and proteins across single cell and spatial modalities. Proximal cell interactions and distinct receptor-ligand pairings identified 7 recurrent cellular communication networks. Robustly mapping reproducible gene signatures across technologies and to a variety of publicly available datasets, we show these highly refined immune subgroups stratify patients with tumour microenvironments associated with prognosis and immunotherapy response. Notably, this reveals that highly infiltrated environments with the potential for immunotherapy response may in fact comprise two distinct communication networks, with differing modes of T cell clonal expansion and immune evasion axes associated with T cell exhaustion or myeloid and NK reprogramming, which could inform targeted combination therapeutic strategies to improve outcomes. Overall, we provide a high-dimensional multi-modal resource dataset that enables cross-platform integration, links stages of T cell clonal expansion with enabling or suppressive RCC immune cell communication networks and nominates rational strategies for combinatorial precision immunotherapy. (Funded by University Health Network, Toronto; REMEDY ClinicalTrials.gov number, NCT04005183.)

cancer biology↗

bioRxiv2026

Humanized patient-derived xenografts preserve tumour-specific immune microenvironments

Defining the genetic and cellular programs that allow solid tumours to evade immune control requires preclinical models that preserve the complexity of the human tumour immune microenvironment. Most available systems capture only part of this biology. Organoid cultures and ex vivo tumour fragments can retain patient-derived tumour architecture and associated immune cells, but immune populations are typically maintained only for short periods. These models also cannot capture antitumour immune responses in the physiological setting of a living organism. Patient-derived xenografts propagated in humanized mice offer a potential path to overcome these limitations by combining patient-derived tumour tissue with a reconstituted human immune system. However, few studies have systematically tested whether these models reproduce the diverse immune cell phenotypes present in the parental tumours from which they are derived. This has limited their use for studying tumour-intrinsic mechanisms that shape immune composition and promote immune evasion. To address this gap, we profiled tumour-infiltrating, splenic, and bone marrow immune cells from ovarian, head and neck, and renal PDX models propagated in CD34+ hematopoietic stem cell (HSC)-derived huNOG-EXL mice expressing human IL-3 and GM-CSF. By comparing tumours grown across distinct HSC donor backgrounds with their matched primary tumour samples, we found that tumour-intrinsic factors are a dominant determinant of immune composition in humanized PDX tumours. Across models, these immune infiltrates generally resembled those of the corresponding parental tumours. These findings support humanized PDX models as a platform for functionally interrogating tumour-intrinsic drivers of immune composition and immune evasion in solid tumours.

cancer biology↗

bioRxiv2026

Extra-lineage tissue programs define the transcription states of human pancreatic cancer

Cancers acquire alternate transcriptional states as they evolve, but the origins, timing and determinants of this plasticity are poorly understood in many tumours. We investigated the transcriptional states of pancreatic cancer by integrating [~]1000 tumour-enriched genomes and transcriptomes from 464 patients combined with scRNA-seq, multiome profiling, and spatial proteomics. Four epithelial states covering the spectrum of lineage plasticity were identified (Classical-1, Classical-2, Basal-1, Basal-2). Comparing these states to normal and pan-cancer human single cell atlases showed each state reflects distinct tissue programs found in other malignancies. Single cell analysis uncovered that the main transcription state of this disease (Classical-1) emerges before KRAS mutations. Spatial proteomics from patients and cancer-free donors showed that the Classical-1 program emerges during acinar-to-ductal metaplasia, and also unexpectedly, in normal ducts without disrupting their morphology. Overall, these findings link the extensive lineage plasticity potential of this organ to the origins of the transcriptional states.

cancer biology↗

bioRxiv2025

Rakaia: interactive discovery of spatial biology at scale

Spatial biology data throughput currently outpaces interpretable analysis, limiting large-scale discovery and translation. Here we present Rakaia, a browser-based platform for multiplexed imaging and spatial transcriptomics, empowering code-free interactive exploration and evaluation. Rakaia enables visualization, annotation, and feature-based querying with prioritization across thousands of images. We use Rakaia to identify cell types of interest in >200 highly multiplexed images from non-malignant human breast tissue. Rakaia is available at: https://rakaia.io/

bioinformatics↗

bioRxiv2025

Integrated spatial proteomics of human PDAC uncovers an expanded tumour-immune-stroma spectrum with genomic associations

Distinctively, pancreatic ductal adenocarcinoma (PDAC) consists of sparse tumour lesions intertwined with extensive desmoplastic stroma. The complexity of tumour-microenvironment interactions within this desmoplasia poses a challenge for accurate tumour profiling and patient stratification, and characterizes a profoundly chemoresistant tumour. Here we mapped the spatial relationships between tumour, stroma, and immune cell compartments delineating tumour and microenvironment types that expand the classical to basal spectrum of human PDAC. We used imaging mass cytometry to profile the in situ multi-cellular organization of 81 cell types in resected cases with paired whole genome sequencing. Cell types, functions, and pathway activation were distributed as highly reproducible environments in discrete locations throughout these tumours, which we deep-profiled using laser-capture mass spectrometry. We show that the connections between tumour phenotypes, vascularization, immune response, and stromal biophysical state are reinforced by genomic aberrations, altered by treatment, and associated with patient outcome. Predictive machine-learning models showed that spatial single cell data outperformed genomic or clinical features but integrated multi-omics models provide the best prediction of patient survival with compressed models requiring only 10 non-redundant robust molecular measures associated with the phenotypic spectrum of PDAC. Together, these findings define a phenotypic and molecular framework of PDAC that captures tumour-microenvironment co-dependencies and offers a refined basis for patient stratification and therapeutic targeting.

cancer biology↗

bioRxiv2025

Automated registration of spatial expression data scales multimodal integration to large cohorts

Recent advances in spatial proteomics enable quantification of the spatial distribution of protein expression across a variety of scales, resolutions, and multiplexing. Registering images from such technologies across modalities is an essential task that enables both the integration of complementary imaging technologies and validation of biological findings. This can be particularly challenging when the modalities capture fundamentally different types of data, such as light intensity, probe counts, or heavy metal counts. However, few datasets and methods address this problem at scale. Here, we introduce the largest dataset to date of cross-modality imaging of both cell line and tissue slides suitable for benchmarking registration methods. We further present Twocan, a Bayesian optimization framework that enables robust automated registration between immunofluorescence imaging and highly multiplex spatial proteomics data. Our method achieves significantly higher registration success rates compared to existing approaches across our comprehensive dataset of 954 image pairs.

bioinformatics↗

bioRxiv2024

Interactive design and validation of antibody panels using single-cell RNA-seq atlases

Single-cell RNA-sequencing holds promise for identifying novel markers of cellular variation for antibody-based technologies. However, antibody panel design is often difficult due to multiple experimental and biological constraints. We introduce Cytomarker, an interactive platform enabling human-in-the-loop design of antibody panels from single-cell transcriptomic data. We use Cytomarker to spatially profile human mammary tissue subpopulations and develop a novel antibody screening approach to validate granular subpopulation predictions across >3.5M cells.

bioinformatics↗

bioRxiv2024

Single-cell analysis of an engineered organoid-based model of pancreatic cancer identifies hypoxia as a contributing factor in the determination of transcriptional subtypes

Pancreatic ductal adenocarcinoma (PDAC) is a high-mortality cancer characterized by its aggressive, treatment-resistant phenotype and a complex tumour microenvironment (TME) featuring significant hypoxia. Bulk transcriptomic analysis has identified the "classical" and "basal-like" transcriptional subtypes which have prognostic value in PDAC; however, it remains unclear how microenvironmental heterogeneity contributes to the expression of these transcriptional signatures. Here, we used single cell transcriptome analysis of the organoid TRACER platform to explore the effect of oxygen and other microenvironmental gradients on PDAC organoid cells. We found that the microenvironmental gradients present in TRACER significantly impact the distribution of organoid transcriptional phenotypes and the enrichment of gene sets linked to cancer progression and treatment resistance. More significantly, we found that microenvironmental gradients drive changes in the expression of the classical and basal-like transcriptional subtype gene signatures. This effect is likely dominated by the oxygen gradients in TRACER, as hypoxia alone induced decreases in the expression of classical marker GATA6 at both the gene and protein level in PDAC cells. This work suggests that hypoxia contributes to determining transcriptional subtypes in PDAC and broadly underscores the importance of considering microenvironmental gradients in organoid-based transcriptomic studies of PDAC.

cancer biology↗

bioRxiv2024

Segmentation error aware clustering for highly multiplexed imaging

Spatial protein expression technologies can map cellular content and organization by simultaneously quantifying the expression of >40 proteins at subcellular resolution within intact tissue sections and cell lines. However, necessary image segmentation to single cells is challenging and error prone, easily confounding the interpretation of cellular phenotypes and cell clusters. To address these limitations, we present STARLING, a novel probabilistic machine learning model designed to quantify cell populations from spatial protein expression data while accounting for segmentation errors. To evaluate performance we developed a comprehensive benchmarking workflow by generating highly multiplexed imaging data of cell line pellet standards with controlled cell content and marker expression and additionally established a novel score to quantify the biological plausibility of discovered cellular phenotypes on patient derived tissue sections. Moreover, we generate spatial expression data of the human tonsil - a densely packed tissue prone to segmentation errors - and demonstrate cellular states captured by STARLING identify known cell types not visible with other methods and enable quantification of intra- and inter- individual heterogeneity. STARLING is available at https://github.com/camlab-bioml/starling.

bioinformatics↗

bioRxiv2023

FABP7 Progenitors are a Targetable Metabolic Root in the BRCA1 Breast

It has been nearly 3 decades since the discovery of the BRCA1/2 genes and their link to breast cancer risk, with prophylactic mastectomy remaining the primary management option for these high-risk mutation carriers. The current paucity of interception strategies is due to undefined, targetable cancer precursor populations in the high-risk breast. Despite known cellular alterations in the BRCA1 breast, epithelial populations at the root of unwarranted cell state transitions remain unresolved. Here, we identify a root progenitor population that is dysregulated in BRCA1 carriers stemming from the metabolic role of BRCA1. This fatty-acid binding protein 7 (FABP7) expressing luminal progenitor population is spatially confined to the mammary ducts, has enhanced clonogenic capacity, and is the predicted origin of mixed basal-luminal differentiation in the BRCA1 but not BRCA2 breast. We show global H3K27 acetylation is reduced within ductal FABP7 cells in BRCA1 carriers in situ, linking to a non-canonical metabolic role of BRCA1 in regulating acetyl-CoA pools and de novo fatty acid synthesis. We demonstrate FABP7 progenitor capacity is preferentially ablated in BRCA1 carriers through inhibition of fatty acid metabolism using an FDA-approved fatty acid synthase (FASN) inhibitor. This study lays the foundation for metabolic control of breast progenitor dynamics to mitigate breast cancer risk in the BRCA1 breast.

cell biology↗

bioRxiv2022

In vivo CRISPR screens reveal Serpinb9 and Adam2 as regulators of immune therapy response in lung cancer

How the genetic landscape of a tumor governs the tumors response to immunotherapy remains largely elusive. Here, we established a direct in vivo CRISPR/Cas9 gene editing methodology to assess the immune-modulatory capabilities of 573 putative cancer genes associated with altered cytotoxic activity in human cancers. Using KrasG12D- and BrafV600E-driven mouse lung cancer models, we identify Serpinb9 and Adam2 as our top immune suppressive and immune enhancing genes, respectively. Mechanistically, we show that Serpinb9 ablation in KrasG12D- and BrafV600E-mutant lung tumor cells greatly enhances the efficacy of cytotoxic T-cells in vitro and in vivo. ADAM2 is a cancer testis antigen broadly expressed in human cancers such as lung adenocarcinoma (13.9%), renal (74.7%), prostate (72.4%), uterine (28.6%) and invasive breast (9.5%) cancer. In our mouse models, we show that Adam2 expression is induced in KrasG12D- but not BrafV600E-driven murine lung tumors and that its expression is further enhanced by immunotherapy. We show that loss of Adam2 significantly decreases KrasG12D-lung tumor burden but blocks the efficacy of cytotoxic T-cells. Consistently, Adam2 overexpression dramatically increases tumor growth and enhances immunotherapy efficacy. Mechanistically, we find that Adam2s oncogenic function depends on modulating the tumor immune microenvironment by restraining productive type I and type II interferon responses as well as cytokine signaling, reducing the presentation of tumor-associated antigen, and modulating surface expression of several immunoregulatory receptors within Kras-driven lung tumors. Adam2 expression also leads to reduced levels of immune checkpoint inhibitors such as Pd-l1, Lag3, Tigit and Tim3. This reduced exhaustion within the tumor microenvironment may explain why ex vivo expanded and adoptively transferred cytotoxic T-cells show enhanced cytotoxic efficacy against Adam2 overexpressing lung tumors. Together, our study highlights the power of integrating cancer genomic with in vivo CRISPR/Cas9 screens to uncover how cancer-associated genetic alterations control responses to immunotherapies.

cancer biology↗

bioRxiv2021

Loss of epigenetic regulation disrupts lineage integrity, reactivatesmultipotency and promotes breast cancer

Systematically investigating the scores of genes mutated in cancer and discerning real drivers from inconsequential bystanders is a prerequisite for Precision Medicine, but remains challenging. Here, we developed a somatic CRISPR/Cas9 mutagenesis screen to study 215 recurrent long-tail breast cancer genes, which revealed epigenetic regulation as major tumor suppressive mechanism. We report that core or accessory components of the COMPASS histone methylase complex including KMT2C, KDM6A, BAP1 and ASXL2 ("EpiDrivers") cooperate with PIK3CAH1047R to transform mouse and human breast epithelial cells. Mechanistically, we find that Cre-mediated activation of PIK3CAH1047R elicited an aberrant alveolar lactation program in luminal cells, which was exacerbated upon loss of EpiDrivers. Remarkably, EpiDriver loss in basal cells also triggered an alveolar-like lineage conversion and accelerated formation of luminal-like tumors, suggesting a basal origin for luminal tumors. As EpiDrivers are mutated in 39% of human breast cancers, lineage infidelity and lactation mimicry may significantly contribute to early steps of breast cancer progression. Statement of significanceInfrequently mutated genes comprise most of the mutational burden in breast tumors but are poorly understood. In-vivo CRISPR screening identified functional tumor suppressors that converged on epigenetic regulation. Loss of epigenetic regulators accelerated tumorigenesis and revealed lineage infidelity and aberrant expression of lactation genes as potential early events in tumorigenesis.

cancer biology↗

bioRxiv2021

Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data

The creation of scalable single-cell and highly-multiplexed imaging technologies that profile the protein expression and phosphorylation status of heterogeneous cellular populations has led to multiple insights into disease processes including cancer initiation and progression. A major analytical challenge in interpreting the resulting data is the assignment of cells to a priori known cell types in a robust and interpretable manner. Existing approaches typically solve this by clustering cells followed by manual annotation of individual clusters or by strategies that gate protein expression at predefined thresholds. However, these often require several subjective analysis choices such as selecting the number of clusters and do not automatically assign cell types in line with prior biological knowledge. They further lack the ability to explicitly assign cells to an unknown or uncharacterized type, which exist in most highly multiplexed imaging experiments due to the limited number of markers quantified. To address these issues we present Astir, a probabilistic model to assign cells to cell types by integrating prior knowledge of marker proteins. Astir uses deep recognition neural networks for fast Bayesian inference, allowing for cell type annotations at the million-cell scale and in the absence of previously annotated reference data across multiple experimental modalities and antibody panels. We demonstrate that Astir outperforms existing approaches in terms of accuracy and robustness by applying it to over 2.1 million single cells from several suspension and imaging mass cytometry and microscopy datasets in multiple tissue contexts. We further showcase that Astir can be used for the fast analysis of the spatial architecture of the tumour microenvironment, automatically quantifying the immune influx and spatial heterogeneity of patient samples. Astir is freely available as an open source Python package at https://www.github.com/camlab-bioml/astir.

bioinformatics↗

bioRxiv2017

Systematic analysis of cell phenotypes and cellular social networks in tissues using the multiplexed image cytometry analysis toolbox (miCAT)

Single-cell, spatially resolved omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source computational histology topography analysis toolbox (histoCAT) to enable the interactive, quantitative, and comprehensive exploration of phenotypes of individual cells, cell-to-cell interactions, microenvironments, and morphological structures within intact tissues. histoCAT will be useful in all areas of tissue-based research. We highlight the unique abilities of histoCAT by analysis of highly multiplexed mass cytometry images of human breast cancer tissues.\n\nTechnological advances in the multi-parametric analysis of single cells have revealed an unprecedented heterogeneity of cellular phenotypes and functional states that are concealed in population-based studies1-3. Each cellular phenotype is defined by the interplay of its internal state and the environment in which ...

systems biology↗