Mudassar Iqbal

Single-cell multi-modal technologies provide powerful means to simultaneously profile cellular states. These are now being employed to study gene regulatory mechanisms in a variety of biological systems. Tailored computational methods for integration and analysis of these data are much needed, with desirable properties in terms of efficiency—to cope with high dimensionality of the data, interpretability—for downstream biological discovery and hypothesis generation, and flexibility—to easily incorporate future modalities. Existing methods cover some but not all of the desirable properties for effective integration and analysis of these data. Here, we present a highly efficient method, q-intNMF, for representation and integration of single-cell multi-modal data using joint non-negative matrix factorization, which can facilitate discovery of linked regulatory topics in each modality. We provide thorough benchmarking using large publicly available datasets against five popular existing methods. q-intNMF performs comparably against the current state-of-the-art methods across a range of metrics. Additionally, q-intNMF provides advantages in terms of computational efficiency and interpretability of discovered regulatory topics in the original feature space. We illustrate this enhanced interpretability in providing insights into cell state changes associated with Alzheimer’s disease. q-intNMF is available as a Python package with extensive documentation and use cases at https://github.com/wmorgans/quick_intNMF.

Spatial transcriptomics is rapidly advancing toward single cell level resolution, revealing complex tissue architectures organized across continuous anatomical gradients. However, accurate identification of spatial domains remains a central computational challenge, as many existing clustering approaches blur anatomical boundaries, merge transitional zones, or fail to resolve localized microstructures. Here we introduce Spartan, an activation-aware multiplex graph framework that explicitly models spatial transitions for high-resolution domain discovery. Spartan integrates spatial topology, Local Spatial Activation (LSA), a neighborhood deviation signal that amplifies localized transcriptomic shifts often attenuated by similarity-based clustering. By jointly modeling cohesion within domains and activation at interfaces, Spartan recovers anatomically aligned partitions across spatially resolved transcriptomics technologies including Visium HD, MERFISH, Stereo-seq, and STARmap. We demonstrate its utility in a high-resolution Visium HD section of developing human esophagus and stomach, where activation-aware graph integration enables precise delineation of transitional regions such as the gastroesophageal junction and supports stable multi-scale domain recovery without fragile hyperparameter tuning. Beyond domain identification, Spartan leverages activation-aware structure to detect spatially variable genes associated with localized tissue remodeling. Spartan scales near-linearly with dataset size, providing a robust and interpretable framework for spatial systems-level analysis.

Acute myeloid leukemia (AML) is characterized by cellular and genetic heterogeneity, which correlates with clinical course. Although single-cell RNA sequencing (scRNA-seq) reflects this diversity to some extent, the low sample numbers in individual studies limit the analytic potential when comparing specific patient groups. We performed large-scale integration of published scRNA-seq datasets to create a unique single-cell transcriptomic atlas for AML (AML scAtlas), totaling 748,679 cells, from 159 AML patients and 51 healthy donors from 20 different studies. This is the largest single-cell data resource for human AML to our knowledge, publicly available at https://cellxgene.cziscience.com/collections/071b706a-7ea7-47a4-bddf-6457725839fc . This AML scAtlas allowed investigations into 20 patients with t(8;21) AML, where we explored the clinical importance of age, given the in-utero origin of pediatric disease. We uncovered age-associated gene regulatory network (GRN) signatures, which we validated using bulk RNA sequencing data to delineate distinct groups with divergent biological characteristics. Furthermore, using an additional multiomic dataset (scRNA-seq and scATAC-seq), we validated our initial findings and created a de-noised enhancer-driven GRN reflecting the previously defined age-related signatures. Applying integrated data analysis of the AML scAtlas, we reveal age-dependent gene regulation in t(8;21) AML, potentially reflecting immature/fetal HSC origin in prenatal origin disease vs postnatal origin. Our analysis revealed that BCLAF1, which is particularly enriched in pediatric AML with t(8;21) of inferred in-utero origin, is a promising prognostic indicator. The AML scAtlas provides a powerful resource to investigate molecular mechanisms underlying different AML subtypes.

Background Acute myeloid leukemia (AML) is characterized by cellular and genetic heterogeneity, which correlates with clinical course. Although single-cell RNA sequencing (scRNA-seq) reflects this diversity to some extent, the low sample numbers in individual studies limit the analytic potential when comparing specific patient groups. Results We performed large scale integration of published scRNA-seq datasets to create a unique single-cell transcriptomic atlas for AML (AML scAtlas), totaling 748,679 cells, from 159 AML patients and 44 healthy donors from 20 different studies. This is the largest single-cell data resource for AML to our knowledge, publicly available at https://cellxgene.bmh.manchester.ac.uk/AML/. This AML scAtlas allowed investigations into 20 patients with t(8;21) AML, where we explored the clinical importance of age, given the in-utero origin of pediatric disease. We uncovered age-associated gene regulatory network (GRN) signatures, which we validated using bulk RNA sequencing data to delineate distinct groups with divergent biological characteristics. Furthermore, using an additional multiomic dataset (scRNA-seq and scATAC-seq), we validated our initial findings and created a de-noised enhancer-driven GRN reflecting the previously defined age-related signatures. Conclusions Applying integrated data analysis of the AML scAtlas, we reveal age-dependent gene regulation in t(8;21) AML, potentially reflecting immature/fetal HSC origin in prenatal origin disease vs postnatal origin. Our analysis revealed that BCLAF1, which is particularly enriched in pediatric AML with t(8;21) of inferred in-utero origin, is a promising prognostic indicator. The AML scAtlas provides a powerful resource to investigate molecular mechanisms underlying different AML subtypes.

Spatially resolved transcriptomics has enabled the study of expression of genes within tissues while retaining their spatial identity. Most spatial transcriptomics (ST) technologies generate a matched histopathological image as part of the standard pipeline, providing morphological information that can complement the transcriptomics data. Here, we present CellPie, a fast, unsupervised factor discovery method based on joint non-negative matrix factorization of spatial RNA transcripts and histological image features. CellPie employs the accelerated hierarchical least squares method to significantly reduce the computational time, enabling efficient application to high-dimensional ST datasets. We assessed CellPie on three different human cancer types with different spatial resolutions, including a highly resolved Visium HD dataset, demonstrating both good performance and high computational efficiency compared to existing methods.

Single-cell multiomics have opened up tremendous opportunities for understanding gene regulatory networks underlying cell states by simultaneously profiling transcriptomes, epigenomes, and proteomes of the same cell. However, existing computational methods for integrative analysis of these high-dimensional multiomics data are either computationally expensive or limited in interpretation. These limitations pose challenges in the implementation of these methods in large-scale studies and hinder a more in-depth understanding of the underlying regulatory mechanisms. Here, we propose TriTan (Triple inTegrative fast non-negative matrix factorization), an efficient joint factorization method for single-cell multiomics data. TriTan implements a highly efficient factorization algorithm, greatly improving its computational performance. Three matrix factorization produced by TriTan helps in clustering cells, identifying signature features for each cell type, and uncovering feature associations across omics, which facilitates the identification of domains of regulatory chromatin and the prediction of cell-type-specific regulatory networks. We applied TriTan to the single-cell multiomics data obtained from different technologies and benchmarked it against the state-of-the-art methods where it shows highly competitive performance. Furthermore, we showed a range of downstream analyses conducted utilizing TriTan outputs, highlighting its capacity to facilitate interpretation in biological discovery.

Background: Acute myeloid leukemia (AML) is characterized by cellular and genetic heterogeneity, which correlates with clinical course. Although single-cell RNA sequencing (scRNA-seq) reflects this diversity to some extent, the low sample numbers in individual studies limit the analytic potential when comparing specific patient groups. Results: We performed large scale integration of published scRNA-seq datasets to create a unique single-cell transcriptomic atlas for AML (AML scAtlas), totaling 748,679 cells, from 159 AML patients and 44 healthy donors from 20 different studies. This is the largest single-cell data resource for AML to our knowledge, publicly available at https://cellxgene.bmh.manchester.ac.uk/AML/ . This AML scAtlas allowed investigations into 20 patients with t(8;21) AML, where we explored the clinical importance of age, given the in-utero origin of pediatric disease. We uncovered age-associated gene regulatory network (GRN) signatures, which we validated using bulk RNA sequencing data to delineate distinct groups with divergent biological characteristics. Furthermore, using an additional multiomic dataset (scRNA-seq and scATAC-seq), we validated our initial findings and created a de-noised enhancer-driven GRN reflecting the previously defined age-related signatures. Conclusions: Applying integrated data analysis of the AML scAtlas, we reveal age-dependent gene regulation in t(8;21) AML, potentially reflecting immature/fetal HSC origin in prenatal origin disease vs postnatal origin. Our analysis revealed that BCLAF1, which is particularly enriched in pediatric AML with t(8;21) of inferred in-utero origin, is a promising prognostic indicator. The AML scAtlas provides a powerful resource to investigate molecular mechanisms underlying different AML subtypes.

Single cell multi-modal technologies provide powerful means to simultaneously profile components of the gene regulatory path-ways of individual cells. These are now being employed to study gene regulatory mechanisms in a variety of biological systems. Tailored computational methods for integration and analysis of these data are much-needed with desirable properties in terms of efficiency -to cope with high dimensionality of the data, inter-pretability -for downstream biological discovery and hypothesis generation, and flexibility -to be able to easily incorporate future modalities. Existing methods cover some but not all of the desirable properties for effective integration of these data. Here we present a highly efficient method, intNMF, for representation and integration of single cell multi-modal data using joint non-negative matrix factorisation which can facilitate discovery of linked regulatory topics in each modality. We provide thorough benchmarking using large publicly available datasets against five popular existing methods. intNMF performs comparably against the current state-of-the-art, and provides advantages in terms of computational efficiency and interpretability of discovered regulatory topics in the original feature space. We illustrate this enhanced interpretability in providing insights into cell state changes associated with Alzheimer’s disease. int-NMF is available as a Python package with extensive documentation and use-cases at https://github.com/wmorgans/quick_intNMF

AIMS: Imaging of tumour habitats is a novel technique to assess regional treatment response by defining tumour ‘habitats’, which are regions with shared imaging characteristics. Here, we describe a novel pipeline to characterise imaging habitats in glioblastoma using bioinformatics and machine learning. We apply this pipeline to glioblastoma patients in the Preoperative Brain Irradiation in Glioblastoma (POBIG) trial that received preoperative radiotherapy for the first time. METHOD: Glioblastoma patients underwent preoperative diffusion/perfusion MRI. A two- stage clustering pipeline was developed to define imaging habitats using relative cerebral blood volume (rCBV) and apparent diffusion co- effcient (ADC). 6 habitats were defined that occupied 10.6-21.5% of tumour volume. Treatment response was evaluated using Ktrans permeability. RESULTS: In newly diagnosed cases the commonest habitats included those with a medium and high rCBV. In contrast, recurrent cases had significantly greater proportion of low rCBV habitats (p = 0.026). Two patients in the POBIG trial received 8 Gy preoperative radiotherapy. Within 24 hours of treatment there was a global decrease in vascularity with a decrease in high rCBV habitats (-3.7%) and increase in low rCBV habitats (+8.2%) in irradiated regions. Habitats demonstrated a heterogeneous response to preoperative radiotherapy, with a greater increase in Ktrans in high rCBV habitats (mean +83%) versus low rCBV habitats (mean +40%). CONCLUSIONS: We have developed a unique pipeline to delineate imaging habitats in glioblastoma and applied it to a novel trial. Imaging habitats can allow regional monitoring of treatment response and could allow more targeted treatment in future.