Biology subjects

Jonsson, P.

Publications and source records attributed to Jonsson, P..

9 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↗

bioRxiv2025

Label-Free Live-Cell Imaging improves Mode of Action Classification

Morphological profiling is a common approach to investigate the modes of action (MOAs) of compounds. Most methods rely on fixed-cell assays, which provide only a single snapshot at a predefined time point and overlook the dynamic nature of cellular responses. In contrast, live-cell imaging tracks responses over time, offering deeper insight into compound-specific effects and mechanisms; however, time-series analysis of image data remains challenging due to limited analytical tools. We present Live Cell Temporal Profiling (LCTP), a workflow for morphological profiling of label-free live-cell time series data that yields interpretable, biologically relevant results. We showcase LCTP in an MOA classification study using label-free data. The workflow integrates established deep-learning components, cell segmentation, live/dead classification, and single-cell feature extraction, with data-driven models to capture MOA-specific temporal phenotypes and produce time-resolved profiles that can be compared across compounds and cell lines. We assess MOA classification performance using double-blinded cross-validation simulating a real-world screening scenario. LCTP significantly improves MOA classification over single-time point analysis, consistently across both cell lines used in the study. Time-resolved phenotypic modeling reveals transient, sustained, and delayed responses, clarifying compound-specific temporal effects and mechanisms across MOAs. The presented workflow is modular: each step removes irrelevant information, enriching signal, and enabling straightforward updates as technologies evolve and as new technologies become available, while supporting reuse across studies broadly. We believe LCTP adds substantial value to high-throughput compound screening, showing that live-cell imaging combined with this workflow yields informative visualizations of temporal effects and improved MOA classification.

bioinformatics↗

bioRxiv2024

OPLS-based Multiclass Classification and Data-Driven Inter-Class Relationship Discovery

Multiclass datasets and large-scale studies are increasingly common in omics sci-ences, drug discovery, and clinical research due to advancements in analytical platforms. Efficiently handling these datasets and discerning subtle differences across multiple classes remains a significant challenge. In metabolomics, two-class OPLS-DA (Orthogonal Projection to Latent Structures Discriminant Analysis) models are widely used due to their strong discrimination capa-bilities and ability to provide interpretable information on class differences. However, these models face challenges in multiclass settings. A common solution is to transform the multiclass comparison into multiple two-class comparisons, which, while more ef-fective than a global multiclass OPLS-DA model, unfortunately results in a manual, time-consuming model-building process with complicated interpretation. Here, we introduce an extension of OPLS-DA for data-driven multiclass classifi-cation: Orthogonal Partial Least Squares-Hierarchical Discriminant Analysis (OPLS-HDA). OPLS-HDA integrates Hierarchical Cluster Analysis (HCA) with the OPLS-DA framework to create a decision tree, addressing multiclass classification challenges and providing intuitive visualization of inter-class relationships. To avoid overfitting and ensure reliable predictions, we use cross-validation during model building. Benchmark results show that OPLS-HDA performs competitively across diverse datasets compared to eight established methods. This method represents a significant advancement, offering a powerful tool to dissect complex multiclass datasets. With its versatility, interpretability, and ease of use, OPLS-HDA is an efficient approach to multiclass data analysis applicable across various fields.

bioinformatics↗

bioRxiv2023

A scalable, data analytics workflow for image-based morphological profiles

Cell Painting is an established community-based, microscopy-assay platform that provides high-throughput, high-content data for biological readouts. In November 2022, the JUMP-Cell Painting Consortium released the largest annotated, publicly available dataset, comprising more than 2 billion cell images. This dataset is designed for predicting the activity and toxicity of 100k drug compounds, with the aim to make cell images as computable as genomes and transcriptomes. In this paper, we have developed a data analytics workflow that is both scalable and computationally efficient, while providing significant, biologically relevant insights for biologists estimating and comparing the effects of different drug treatments. The two main objectives proposed include: 1) a simple, yet sophisticated, scalable data analytics metric that utilizes negative controls for comparing morphological cell profiles. We call this metric the equivalence score (Eq. score). 2) A workflow to identify and amplify subtle morphological image profile changes caused by drug treatments, compared to the negative controls. In summary, we provide a data analytics workflow to assist biologists in interpreting high-dimensional image features, not necessarily limited to morphological ones. This enhances the efficiency of drug candidate screening, thereby streamlining the drug development process. By increasing our understanding of using complex image-based data, we can decrease the cost and time to develop new, life-saving treatments. Author summaryMicroscopy-assays are often used to study cell responses to treatments in the search for new drugs. In this paper, we present a method that simplifies the understanding of the data generated from such assays. The data in this study consists of 750 morphological features, which describe the traits and characteristics of the cells, extracted from the images. By using untreated cells as a biological baseline, were able to detect subtle changes that occur in the treated cells. These changes are then transformed into an equivalence score (Eq. score), a metric that lets us compare the similarities among different treatments relative to our baseline of untreated cells. Our Eq. score approach transforms complex, high-dimensional data about cell morphology into something more interpretable and understandable. It reduces the "noise" in the features and highlights important changes, the "signal". Our method can be integrated into existing workflows, aiding researchers in understanding and interpreting complex morphological data derived from cell images more easily. Understanding cell morphology is crucial to deepening our knowledge of biological systems. Ultimately, this could contribute to the faster and more cost-effective development of new, life-saving treatments.

cell biology↗

bioRxiv2020

Anatomic position determines oncogenic specificity in melanoma

Oncogenic alterations to DNA are not transforming in all cellular contexts1,2. This may be due to pre-existing transcriptional programs in the cell of origin. Here, we define anatomic position as a major determinant of why cells respond to specific oncogenes. Cutaneous melanoma arises throughout the body, whereas the acral subtype arises on the palms of the hands, soles of the feet, or under the nails3. We sequenced the DNA of cutaneous and acral melanomas from a large cohort of human patients and found a specific enrichment for BRAF mutations in cutaneous melanoma but CRKL amplifications in acral melanoma. We modeled these changes in transgenic zebrafish models and found that CRKL-driven tumors predominantly formed in the fins of the fish. The fins are the evolutionary precursors to tetrapod limbs, indicating that melanocytes in these acral locations may be uniquely susceptible to CRKL. RNA profiling of these fin/limb melanocytes, compared to body melanocytes, revealed a positional identity gene program typified by posterior HOX13 genes. This positional gene program synergized with CRKL to drive tumors at acral sites. Abrogation of this CRKL-driven program eliminated the anatomic specificity of acral melanoma. These data suggest that the anatomic position of the cell of origin endows it with a unique transcriptional state that makes it susceptible to only certain oncogenic insults.

cancer biology↗

bioRxiv2018

Hydrodynamic trapping measures the interaction between membrane-associated molecules

How membrane proteins distribute and behave on the surface of cells is determined by the molecules interaction potential. However, measuring this potential, and how it varies with protein-to-protein distance, has been challenging. We here present how a method we call hydrodynamic trapping can achieve this. Our method uses the focused liquid flow from a micropipette to locally accumulate molecules protruding from a lipid membrane. The interaction potential, as well as information about the dimensions of the studied molecule, are obtained by relating the degree of accumulation to the strength of the trap. We have used this to study four representative proteins, with different height-to-width ratios and protein properties; from the globular streptavidin, to the rod-like immune cell proteins CD2, CD4 and CD45. The obtained data illustrates how protein shape, glycosylation and flexibility influence the behaviour of membrane proteins as well as underline the general applicability of the method.

biophysics↗

bioRxiv2018

Statistical loadings and latent significance simplify and improve interpretation of multivariate projection models

Multivariate projection methods are unique in being both multivariable by combining many variables into stronger predictive features (latent variables), and multivariate for being able to model systematic variation both related and orthogonal to an observed response. Orthogonal partial least squares (OPLS) is a versatile multivariate projection method for analysis of correlation, discrimination and effect changes. However, currently OPLS is not fully using its multivariate potential since orthogonal systematic variation is not considered in model interpretation, resulting in univariate interpretation of variable significance. We present a strategy for improved interpretation of OPLS models based upon a post-hoc linear regression analysis that can be used with or without the orthogonal OPLS score(s) as a covariate to make the interpretation multivariate or univariate respectively. By selecting the observed response y or estimated response yhat as a one of the factors in the linear regression the results are related to either of the OPLS loadings w or p. Furthermore, converting the OPLS loading values to statistical t-values creates a direct link to statistical significance. Finally, by applying three different Boolean loadings W, P and W{wedge}P variable significance can be summarized based on three criteria. W and P reveal if the values in w or p respectively are outside the statistical limits with W{wedge}P being the logical conjunction of W and P (significant if outside limits in both W and P). Two examples are used to verify the proposed strategy. First, a synthetic example, simulating a mix of mass spectra, and second a clinical metabolomics study of a dietary intervention. In the simulated example we show that multivariate interpretation gives higher accuracy for estimation of true differences, mainly due to higher true positive rate. Furthermore, we highlight how application of W{wedge}P for summarizing variable significance leads to higher accuracy. For the metabolomics example, we show that a more detailed interpretation, i.e. larger number of significant metabolites of relevance, is obtained using the multivariate interpretation. In summary, the suggested strategy provides means for facilitated interpretation of OPLS models, beyond univariate statistics, and offers a multivariate tool for discovery of biomarker patterns, i.e. latent biomarkers.

bioinformatics↗

bioRxiv2018

Genetic meta-analysis identifies 10 novel loci and functional pathways for Alzheimer’s disease risk

Late onset Alzheimers disease (AD) is the most common form of dementia with more than 35 million people affected worldwide, and no curative treatment available. AD is highly heritable and recent genome-wide meta-analyses have identified over 20 genomic loci associated with AD, yet only explaining a small proportion of the genetic variance indicating that undiscovered loci exist. Here, we performed the largest genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 AD cases, 383,378 controls). AD-by-proxy status is based on parental AD diagnosis, and showed strong genetic correlation with AD (rg=0.81). Genetic meta analysis identified 29 risk loci, of which 9 are novel, and implicating 215 potential causative genes. Independent replication further supports these novel loci in AD. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver and microglia). Furthermore, gene-set analyses indicate the genetic contribution of biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomisation results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying more of the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD to guide new drug development.

genetics↗

bioRxiv2017

Constraining CD45 exclusion at close-contacts provides a mechanism for discriminatory T-cell receptor signalling

The T-cell receptor (TCR) triggers the elimination of pathogens and tumors by T lymphocytes. In order for this to avoid damage to the host, the receptor has to discriminate between thousands of peptide ligands presented by each host cell. Exactly how the TCR does this is unknown. In resting T-cells, the TCR is largely unphosphorylated due to the dominance of phosphatases over kinases expressed at the cell surface. When agonist peptides are presented to the TCR by major histocompatibility complex (MHC) proteins expressed by antigen-presenting cells (APCs), very fast receptor triggering occurs, leading to TCR phosphorylation. Recent work suggests that this depends on the local exclusion of the phosphatases from regions of contact of the T cells with the APCs. Here, we develop and test a quantitative treatment of receptor triggering reliant only upon TCR dwell-time in phosphatase-depleted cell-cell contacts constrained in area by cell topography. Using the model and experimentally-derived parameters, we find that ligand discrimination is possible but that it depends crucially on individual contacts being 400 nm in diameter or smaller, i.e. the size generated by microvilli. The model not only correctly predicts the relative signaling potencies of known agonists and non-agonists, but achieves this in the absence of conventional, multi-step kinetic proof-reading. Our work provides a simple, quantitative and predictive molecular framework for understanding why TCR triggering is so selective and fast, and reveals that for some receptors, cell topography crucially influences signaling outcomes.\n\nSignificance statementOne approach to testing biological theories is to determine if they are predictive. A simple, theoretical treatment of TCR triggering suggests that ligand discrimination by the receptor relies on just two physical principles: (1) the time TCRs spend in cell-cell contacts depleted of large tyrosine phosphatases; and (2) constraints on contact size imposed by T cells using finger-like protrusions to interrogate their targets. The theory not only allows agonistic and non-agonistic TCR ligands to be distinguished but predicts the relative signalling potencies of agonists with remarkable accuracy. This suggests that the theory captures the essential features of receptor triggering.

immunology↗