Biology subjects

Dufva, O.

Publications and source records attributed to Dufva, O..

12 recordsLinked to original sources

bioRxiv2026

STEER-seq: a recombinase based platform for pooled multimodal single-cell perturbation screening

Pooled gain-of-function screening is a powerful method for screening for transcription factors whose overexpression leads to changes in cellular identity. However, variable transgene copy number, integration site and genomic context from random viral integration can confound quantitative comparisons between perturbations. We present STEER-seq (Single-cell Transgene Expression via Exchange Recombination sequencing), a virus-free platform for pooled ORF gain-of-function screening in human induced pluripotent stem cells (hiPSCs). STEER-seq combines irreversible Bxb1-mediated integration at a defined genomic safe-harbour locus with promoterless donor libraries and barcode-enabled perturbation tracking, enabling single-copy expression from a common promoter and genomic context and direct recovery of perturbation identity by single-cell sequencing. In a focused haematopoietic screen, SPI1 expression alone was sufficient to drive differentiation towards HSC/MPP-like progenitors in the absence of exogenous cytokines, with transient induction generating progenitors that retained multilineage haematopoietic differentiation potential. Joint RNA-chromatin accessibility profiling linked transcription factor-induced transcriptional responses to chromatin remodelling and enabled reconstruction of perturbation-induced gene regulatory networks, including a RUNX2-driven osteoblast programme. Scaling STEER-seq to around 2,000 human coding sequences enabled systematic discovery of transcription factors capable of inducing distinct cell-state programmes, including neural progenitor-, melanocyte- and vascular smooth muscle-like states, as well as maintenance of pluripotency. STEER-seq provides a scalable, quantitative framework for single-cell and multimodal gain-of-function screening, enabling functional discovery and engineering of human cell identity.

genomics↗

bioRxiv2026

A Community-Driven Single-Cell PBMC Reference Integrating Landmark Datasets Spanning Health and Disease

Blood provides an accessible window into human health, yet the absence of a peripheral blood mononuclear cells (PBMCs) reference with standardized immune cell annotations has constrained comparisons between single-cell RNA sequencing (scRNAseq) studies. Here, we present HARP (Human Cell Atlas Reference for PBMCs), an integrated atlas of ~9 million PBMCs from >2,600 donors across 15 studies spanning four continents, encompassing diverse populations, neonates to 97 years, across health and diverse immune-related diseases. We developed optimized integration workflows, including novel label-free metrics to assess integration quality, and generated community-driven consensus annotations for 192 immune cell subsets, identifying rare populations representing as few as 0.004% of PBMCs. HARP reveals coordinated cellular modules associated with age, sex, and disease, identifies female-biased interferon and inflammatory gene programs and their perturbation by COVID-19, and uncovers a novel sexual dimorphism in prostaglandin signaling. Finally, we introduce scTiger, a hierarchical label-transfer framework that accurately projects these 192 cell annotations onto ~18 million additional PBMCs, providing a robust and transferable reference for harmonizing future PBMC studies. HARP provides a high-resolution community-driven reference atlas for PBMC annotation and analysis, as a basis for emerging clinical applications of single-cell genomics.

immunology↗

bioRxiv2026

Selective scoring of drug effects in multicellular co-culture systems

Multicellular co-culture screening reveals compound effects that depend on cell-cell interactions. Standard dose-response metrics fail to resolve effects that arise either from target-effector cell interactions or from non-specific toxic effects. Here, we developed Co-culture Efficacy Score (CES), a robust computational framework that enables systematic identification of compounds that selectively modulate cellular interactions in multicellular assays. CES framework supports both therapeutic scoring that penalizes direct effector cell toxicity, as well as a mechanistic discovery that estimates immunomodulatory effects by adjusting for effector cell responses. When screening 527 compounds across 10 hematological cancer models co-cultured with natural killer (NK) cells, CES distinguished co-culture-specific immunomodulatory effects from NK cell toxicity and cancer cell inhibitory responses, recovering systematic enhancer and inhibitory patterns. We further assessed CES robustness using higher-resolution validation screens and demonstrated its applicability to identify selective compounds in anti-CD19 CAR T-cell and antiviral host-pathogen screens. To facilitate its broad use, we implemented CES as an interactive web-application for quantitative analysis of compound responses in co-culture assays, providing a widely applicable scoring framework for cancer immunotherapy, antiviral screening and drug discovery.

bioinformatics↗

bioRxiv2026

Multimodal immunopharmacologic screens identify drugs rewiring the cancer-immune interface

Natural killer (NK) cell-based therapies are a promising approach in cancer, but their efficacy is limited by impaired effector function and tumor-intrinsic resistance. To systematically identify therapeutic strategies that target both sides of the cancer-immune interface, we designed a multimodal immunopharmacologic screening platform comprising high-throughput co-culture drug screens, cytokine secretome profiling, single-cell perturbation screens, and genome-scale CRISPR screening, followed by validation in biobanked patient-derived models. Applying the platform across five blood cancer types, we identified protein kinase C (PKC) activation to simultaneously increase effector cytotoxicity and cytokine secretion through transcriptomic rewiring, and tumor susceptibility to NK cell killing through tumor-intrinsic PKC-{delta}. In patient samples, PKC activation sensitized NK-resistant leukemic progenitors to NK cell killing. In addition, NEDD8 inhibition enhanced NK function and shifted tumor TNF signaling towards pro-apoptotic pathways. Our platform provides a systematic approach to identify drugs rewiring both sides of the cancer-immune interface to circumvent tumor immune resistance.

cancer biology↗

bioRxiv2026

Single-Cell Profiling Reveals Developmental Trajectories and identifies SYK and TIM3 as Targets in some T Cell Lymphomas

T cell lymphomas (TCL) are a heterogeneous collection of malignancies whose origins and pathogenesis are poorly understood and for which few efficacious therapeutic options exist. Here, we conduct single-cell transcriptomic profiling spanning eight TCL entities and describe entity-associated programmes. We predict the cell of origin for these tumours through an integrative analysis of transcriptome and T cell receptor (TCR) maturation states. By identifying tumours with TCR states ranging from the pre-TCR through non-productive and productive TCR alpha and beta chain rearrangements we shed new light on their developmental origins. Furthermore, we apply our drug2cell computational drug target predictions with drug screens using patient-derived cell models, systematically benchmarking the performance of drug2cell and validating compounds and targets. This process identifies SYK inhibitors as a therapeutic opportunity and prioritises TIM3 for immunotherapy based on combined spatial transcriptomics analysis. Overall, our data provide a resource for diagnostics and therapies for tumours of critical unmet need.

cancer biology↗

bioRxiv2026

Perturbation-guided mapping of colorectal cancer cell states to causal mechanisms

Colorectal cancer (CRC) cell atlases have refined descriptive maps of tumour ecosystems, yet cross-sample integration often obscures disease-relevant patient-specific variation and remains largely correlative, limiting insight into the mechanisms and state transitions that drive progression and treatment response. Here, we develop a continual learning framework to construct a comparative single-cell CRC atlas spanning over 300 patients and 1.5 million cells, preserving inter-patient variation while aligning healthy and malignant contexts. We resolve distinct non-canonical malignant cell states, including an endoderm-like state enriched in microsatellite-stable, KRAS-mutant CRC with features of oncofetal plasticity. Cell states are recapitulated in patient-derived organoids, establishing a tractable model of reprogramming. By linking the observational atlas to a large-scale perturbation atlas using relative representations, we map perturbations that drive cells toward defined phenotypic extremes. We connect cell states to therapeutic responses, showing that MAPK inhibition induces a shift away from a proliferative phenotype and converges towards a plastic, endoderm-like state. Together, this framework moves beyond static atlases to enable mechanistic modeling of cell-state regulation and causal inference toward cell-state-directed therapies.

genomics↗

bioRxiv2025

Integrating multi-covariate disentanglement with counterfactual analysis on synthetic data enables cell type discovery and counterfactual predictions.

Single-cell gene expression is influenced by diverse covariates such as genomics protocol, tissue origin, donor attributes, and microenvironment, which are challenging to disentangle. We present CellDISECT, a novel method combining disentangled representations and causal inference for multi-batch, multi-covariate single-cell data analysis. CellDISECT employs a mixture of expert variational autoencoders to learn covariate-specific and unsupervised latent spaces, enabling counterfactual predictions and biological discovery. Drawing inspiration from LLM training on synthetic data, CellDISECT generates synthetic counterfactuals during training and their quality is scored in the loss function. This semi-autoencoding of counterfactuals during training increases model performance in counterfactual predictions at test time. Benchmarking across datasets, CellDISECT outperformed existing methods in disentanglement, counterfactual in-silico prediction of responses to perturbations, and cell type discovery. CellDISECT predicted responses of cells to changing tissue microenvironments and identified a novel pre-natal megakaryocyte subpopulation with immune characteristics distinct from classical platelet-producing MKs, highlighting its unique capabilities in single-cell analysis to help identify novel subpopulations and reduce concerns of technical effects during integration.

genomics↗

bioRxiv2023

Pan-cancer profiling of tumor-infiltrating natural killer cells through transcriptional reference mapping

The functional diversity of natural killer (NK) cell repertoires stems from differentiation, homeostatic receptor-ligand interactions, and adaptive-like responses to viral infections. Here, we generated a single-cell transcriptional reference map of healthy human blood and tissue-derived NK cells, with temporal resolution and fate-specific expression of gene regulator networks defining NK cell differentiation. Using transfer learning, transcriptomes of tumor-infiltrating NK cells from seven solid tumor types (427 patients), combined from 39 datasets, were incorporated into the reference map and interrogated for tumor microenvironment (TME)-induced perturbations. We identified six functionally distinct NK cellular states in healthy and malignant tissues, two of which were commonly enriched for across tumor types: a dysfunctional stressed CD56bright state susceptible to TME-induced immunosuppression and a cytotoxic TME-resistant effector CD56dim state. The ratio of stressed CD56bright and effector CD56dim was predictive of patient outcome in malignant melanoma and osteosarcoma. This resource may inform the design of novel NK cell therapies and can be extended endlessly through transfer learning to interrogate new datasets from experimental perturbations or disease conditions.

immunology↗

bioRxiv2022

Single-cell functional genomics of natural killer cell evasion in blood cancers

Natural killer (NK) cells are emerging as a promising therapeutic option in cancer. To better understand how cancer cells evade NK cells, we studied interacting NK and blood cancer cells using single-cell and genome-scale functional genomics screens. At single-cell resolution, interaction of NK and cancer cells induced distinct activation states in both cell types depending on the cancer cell lineage and molecular phenotype, ranging from more sensitive myeloid to more resistant B-lymphoid cancers. CRISPR screens uncovered cancer cell-intrinsic genes driving sensitivity and resistance, including antigen presentation and death receptor signaling mediators, adhesion molecules, protein fucosylation genes, and transcriptional regulators. CRISPR screens with a single-cell transcriptomic readout revealed how these cancer cell genes influenced the gene expression landscape of both cell types, including regulation of activation states in both cancer and NK cells by IFN{gamma} signaling. Our findings provide a resource for rational design of NK cell-based therapies in blood cancers. HIGHLIGHTSO_LITranscriptomic states of interacting NK cells and cancer cells depend on cancer cell lineage C_LIO_LIMolecular correlates of increased sensitivity of myeloid compared to B-lymphoid cancers include activating receptor ligands NCR3LG1, PVR, and ULBP1 C_LIO_LINew regulators of NK cell resistance from 12 genome-scale CRISPR screens include blood cancer-specific regulators SELPLG, SPN, and MYB C_LIO_LISingle-cell transcriptomics CRISPR screens targeting 65 genome-wide screen hits identify MHC-I, IFNy, and NF-{kappa}B regulation as underlying mechanisms C_LI

immunology↗

bioRxiv2022

Tracing back primed resistance in cancer via sister cells

Exploring non-genetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods. However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. We developed ReSisTrace that uses shared transcriptomic features of synchronised sister cells to predict the states that prime treatment resistance. We applied ReSisTrace in ovarian cancer cells perturbed with olaparib, carboplatin or natural killer (NK) cells. The pre-resistant phenotypes were defined by cell cycle and proteostatic features, reflecting the traits enriched in the upcoming subclonal selection. Furthermore, DNA repair deficiency rendered cells susceptible to both DNA damaging agents and NK killing in a context-dependent manner. Finally, we leveraged the pre-resistance profiles to predict and validate small molecules driving cells to sensitive states prior to treatment. In summary, ReSisTrace resolves pre-existing transcriptional features of treatment vulnerability, facilitating both molecular patient stratification and discovery of synergistic pre-sensitizing therapies.

cancer biology↗

bioRxiv2020

Nintedanib Targets KIT D816V Neoplastic Cells Derived from Induced Pluripotent Stem cells of Systemic Mastocytosis

The KIT D816V mutation is found in more than 80% of patients with systemic mastocytosis (SM) and is key to neoplastic mast cell (MC) expansion and accumulation in affected organs. KIT D816V therefore represents a prime therapeutic target for SM. Here we generated a panel of patient-specific KIT D816V induced pluripotent stem cells (iPSCs) from patients with aggressive SM (ASM) and mast cell leukemia (MCL) to develop a patient-specific SM disease model for mechanistic and drug discovery studies. KIT D816V iPSCs differentiated into neoplastic hematopoietic progenitor cells and MCs with patient-specific phenotypic features, thereby reflecting the heterogeneity of the disease. CRISPR/Cas9n-engineered KIT D816V human embryonic stem cells (ESCs), when differentiated into hematopoietic cells, recapitulated the phenotype observed for KIT D816V iPSC hematopoiesis. KIT D816V causes constitutive activation of the KIT tyrosine kinase receptor and we exploited our iPSCs and ESCs to investigate new tyrosine kinase inhibitors targeting KIT D816V. Our study identified nintedanib as a novel KIT D816V inhibitor. Nintedanib selectively reduced the viability of iPSC-derived KIT D816V hematopoietic progenitor cells and MCs in the nanomolar range. Nintedanib was also active on primary samples of KIT D816V SM patients. Molecular docking studies show that nintedanib binds to the ATP binding pocket of inactive KIT D816V. Our results suggest nintedanib as a new drug candidate for KIT D816V targeted therapy of advanced SM.

cancer biology↗

bioRxiv2019

Immunogenomic landscape of hematological malignancies

Understanding factors that shape the immune landscape across hematological malignancies is essential for immunotherapy development. Here, we integrated over 8,000 transcriptomes and over 1,000 samples with multilevel genomic data of hematological cancers to investigate how immunological features are linked to cancer subtypes, genetic and epigenetic alterations, and patient survival. Infiltration of cytotoxic immune cells was associated with distinct microenvironmental responses and driver alterations in different cancers, such as TP53 in acute myeloid leukemia and DTX1 in diffuse large B cell lymphoma. Epigenetic modification of CIITA regulating antigen presentation, cancer type-specific immune checkpoints such as VISTA in myeloid malignancies, and variation in cancer antigen expression further contributed to immune heterogeneity. Prognostic models highlighted the significance of immunological properties in predicting survival. Our study represents the most comprehensive effort to date to link immunology with cancer subtypes and genomics in hematological malignancies, providing a resource to guide future studies and immunotherapy development.

immunology↗