Biology subjects

Olsen, L. R.

Publications and source records attributed to Olsen, L. R..

13 recordsLinked to original sources

bioRxiv2026

AI supported in silico screening of chimeric antigen receptor therapy targets

Chimeric antigen receptor (CAR) cell therapy has achieved transformative clinical success through targeting of CD19 in refractory B cell malignancies, but extension of this strategy to solid tumors, other hematological malignancies, and autoimmune disease has exposed the complexity of target selection. Antigen abundance alone is not sufficient to define a suitable CAR target. Instead, therapeutic efficacy and safety are shaped by a broader set of molecular features, including isoform usage, subcellular localization, secretion, epitope stability, and the structural context in which antibody-derived binding domains engage their target. At the same time, advances in transcriptomics, structural biology, and artificial intelligence (AI)-enabled prediction now make it possible to assess many of these properties systematically. Here, we outline the principal molecular features that characterize effective and safe CAR targets and present a practical framework that integrates public datasets with computational and AI-based tools for their evaluation. Using HER2 as an illustrative case, we show how isoform-resolved expression, single-cell analyses, topology prediction, structure modelling, epitope mapping, and in silico binding analyses can reveal liabilities that are not captured by conventional target-expression screens alone. This framework provides a systematic strategy to prioritize targets and epitopes, guide preclinical investigation, and de-risk clinical translation. We anticipate that such integrative workflows will become increasingly important for moving CAR target discovery from descriptive expression analysis towards informed therapeutic design.

bioinformatics↗

bioRxiv2026

A Reproducible and Extensible Benchmark of Supervised Cell Type Annotation Tools for Cytometry Data

High-dimensional cytometry technologies such as flow cytometry (FCM) and mass cytometry (CyTOF) are central to immunophenotyping in research and clinical practice. While manual gating remains the standard for cell population annotation, it is time-consuming, difficult to scale, and subject to inter-operator variability. Supervised annotation methods have emerged as a way of scaling manual annotation work, yet independent benchmarks for comparing these tools remain limited and quickly become outdated. This study presents a reproducible and extensible benchmark of supervised cytometry annotation tools implemented within the OmniBenchmark framework. Five supervised annotation methods were evaluated, spanning linear models, nearest-neighbor approaches, tree-based classifiers, mixture-rule systems, and deep learning, across eight publicly available datasets carefully selected to cover technologies, tissues, panel designs, and healthy and disease contexts. Using a sample-centric cross-validation design that reflects common reference-mapping scenarios, overall and per-population F1 scores, performance on rare populations, runtime, and robustness to reduced training set sizes was tested. Performance varied substantially across datasets and was not fully explained by dataset size or dimensionality, highlighting both operator dependence in annotation and the importance of biological context, cohort heterogeneity, and population imbalance. Less prevalent populations (<1%) remained a key challenge for most methods. Downsampling analyses showed that moderate reference sizes were often sufficient to achieve near-maximum performance. Rather than ranking methods, this benchmark provides a standardized and transparent framework for evaluating annotation tools under realistic deployment conditions. As a living resource, the OmniBenchmark implementation supports continuous integration of new datasets, tools, and metrics for both tool developers and end users annotating datasets. This enables ongoing, reproducible method comparison and informed tool selection for diverse cytometry applications.

bioinformatics↗

bioRxiv2026

X-Plat: A polynomial regression based tool for cross-platform transformation of expression and methylation data

Many biological studies could benefit from combining data from legacy microarray and high-throughput sequencing platforms, especially in clinical domains where collecting additional samples is not possible. However, incompatibility between platforms makes legacy data difficult to integrate, owing to differences in platform design, target preparation, and dependence on prior annotations. Here, we describe X-Plat, a cross-platform data transformation tool for both expression and methylation assays that inter-converts data between microarray and sequencing platforms using per-gene second-degree polynomial regression. X-Plat learns cross-platform conversion rules from paired microarray-sequencing datasets spanning multiple conditions, sample sources, organisms, and platforms, and evaluates performance using cross-validated root mean square error (RMSE) per gene. In rat, Arabidopsis, and human datasets, X-Plat achieved lower cross-validated RMSE than TDM, HARMONY, and HARMONY2 for the vast majority of genes ([≥]95% in all sequencing-to-array transformations and most array-to-sequencing transformations, with [~]82% in the Arabidopsis array-to-sequencing setting), and these findings were confirmed using RMSE on held-out test samples from the first cross-validation fold. X-Plat also achieved low RMSE ([≤]0.2) for the majority of CpG regions in paired human array-sequencing methylation datasets. Using X-Plat, users can transform data between microarray and high-throughput sequencing platforms, enabling cross-platform comparison and reuse of legacy cohorts. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/707273v2_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@30cabeorg.highwire.dtl.DTLVardef@14ea2eborg.highwire.dtl.DTLVardef@1aa232forg.highwire.dtl.DTLVardef@ebf4f3_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular Therapy

Understanding how intra-tumoral immune populations coordinate to generate anti-tumor responses following therapy can guide precise treatment prioritization. We performed systematic dissection of an established adoptive cellular therapy, donor lymphocyte infusion (DLI), by analyzing 348,905 single-cell transcriptomes from 74 longitudinal bone-marrow samples of 25 patients with relapsed myeloid leukemia; a subset was evaluated by protein-based spatial analysis. In acute myelogenous leukemia (AML) responders, diverse immune cell types within the bone-marrow microenvironment (BME) were predicted to interact with a clonally expanded population of ZNF683+GZMB+ CD8+ cytotoxic T lymphocytes (CTLs) which demonstrated in vitro specificity for autologous leukemia. This population, originating predominantly from the DLI product, expanded concurrently with NK and B cells. AML nonresponder BME revealed a paucity of crosstalk and elevated TIGIT expression in CD8+ CTLs. Our study highlights recipient BME differences as a key determinant of effective anti-leukemia response and opens new opportunities to modulate cell-based leukemia-directed therapy.

cancer biology↗

bioRxiv2023

A systems vaccinology resource to develop and test computational models of immunity

Computational models that predict an individuals response to a vaccine offer the potential for mechanistic insights and personalized vaccination strategies. These models are increasingly derived from systems vaccinology studies that generate immune profiles from human cohorts pre- and post-vaccination. Most of these studies involve relatively small cohorts and profile the response to a single vaccine. The ability to assess the performance of the resulting models would be improved by comparing their performance on independent datasets, as has been done with great success in other areas of biology such as protein structure predictions. To transfer this approach to system vaccinology studies, we established a prototype platform that focuses on the evaluation of Computational Models of Immunity to Pertussis Booster vaccinations (CMI-PB). A community resource, CMI-PB generates experimental data for the explicit purpose of model evaluation, which is performed through a series of annual data releases and associated contests. We here report on our experience with the first such dry run for a contest where the goal was to predict individual immune responses based on pre-vaccination multi-omic profiles. Over 30 models adopted from the literature were tested, but only one was predictive, and was based on age alone. The performance of new models built using CMI-PB training data was much better, but varied significantly based on the choice of pre-vaccination features used and the model building strategy. This suggests that previously published models developed for other vaccines do not generalize well to Pertussis Booster vaccination. Overall, these results reinforced the need for comparative analysis across models and datasets that CMI-PB aims to achieve. We are seeking wider community engagement for our first public prediction contest, which will open in early 2024.

systems biology↗

bioRxiv2023

Building flexible and robust analysis frameworks for molecular subtyping of cancers

Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in-depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray vs. RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification, and reporting and a use case of the framework in breast cancer.

bioinformatics↗

bioRxiv2022

Expression and Splicing Mediate Distinct Biological Signals

BackgroundThrough alternative splicing, most human genes produce multiple isoforms in a cell-, tissue-, and disease-specific manner. Numerous studies show that alternative splicing is essential for development, diseases and their treatments. Despite these important examples, the extent and biological relevance of splicing are currently unknown. ResultsTo solve this problem, we developed pairedGSEA and used it to profile transcriptional changes in 100 representative RNA-seq datasets. Our systematic analysis demonstrates that changes in splicing, on average, contribute to 48.1% of the biological signal in expression analyses. Gene-set enrichment analysis furthermore indicates that expression and splicing both convey shared and distinct biological signals. ConclusionThese findings establish alternative splicing as a major regulator of the human condition and suggest that most contemporary RNA-seq studies likely miss out on critical biological insights. We anticipate our results will contribute to the transition from a gene-centric to an isoform-centric research paradigm.

molecular biology↗

bioRxiv2022

Target isoforms are an overlooked challenge and opportunity in chimeric antigen receptor cell therapy

The development of novel chimeric antigen receptor (CAR) cell therapies is rapidly growing, with 299 new agents being reported and 109 new clinical trials initiated so far this year. One critical lesson from approved CD19-specific CAR therapies is that target isoform switching has been shown to cause tumor relapse, but little is known about the isoforms of CAR targets in solid cancers. Here we assess the protein isoform landscape and identify both the challenges and opportunities protein isoform switching present as CAR therapy is applied to solid cancers.

cancer biology↗

bioRxiv2021

Robust integration of single-cell cytometry datasets

Combining single-cell cytometry datasets increases the analytical flexibility and the statistical power of data analyses. However, in many cases the full potential of co-analyses is not reached due to technical variance between data from different experimental batches. Here, we present cyCombine, a method to robustly integrate cytometry data from different batches, experiments, or even different experimental techniques, such as CITE-seq, flow cytometry, and mass cytometry. We demonstrate that cyCombine maintains the biological variance and the structure of the data, while minimizing the technical variance between datasets. cyCombine does not require technical replicates across datasets, and computation time scales linearly with the number of cells, allowing for integration of massive datasets. Robust, accurate, and scalable integration of cytometry data enables integration of multiple datasets for primary data analyses and the validation of results using public datasets.

bioinformatics↗

bioRxiv2021

A comparison of tools for copy-number variation detection in germline whole exome and whole genome sequencing data

BackgroundCopy-number variations (CNVs) have important clinical implications for several diseases and cancers. The clinically relevant CNVs are hard to detect because CNVs are common structural variations that define large parts of the normal human genome. CNV calling from short-read sequencing data has the potential to leverage available cohort studies and allow full genomic profiling in the clinic without the need for additional data modalities. Questions regarding performance of CNV calling tools for clinical use and suitable sequencing protocols remain poorly addressed, mainly because of the lack of good reference data sets. MethodsWe reviewed 50 popular CNV calling tools and included 11 tools for benchmarking in a unique reference cohort encompassing 39 whole genome sequencing (WGS) samples paired with analysis by the current clinical standard --SNP-array based CNV calling. Additionally, for nine of these samples we performed whole exome sequencing (WES) performed, in order to address the effect of sequencing protocol on CNV calling. Furthermore, we included Gold Standard reference sample NA12878, and tested 12 samples with CNVs confirmed by multiplex ligation-dependent probe amplification (MLPA). ResultsTool performance varied greatly in the number of called CNVs and bias for CNV lengths. Some tools had near-perfect recall of CNVs from arrays for some samples, but poor precision. Filtering output by CNV ranks from tools did not salvage precision. Several tools had better performance patterns for NA12878, and we hypothesize that this is the result of overfitting during the tool development. ConclusionsWe suggest combining tools with the best recall: GATK gCNV, Lumpy, DELLY, and cn.MOPS. These tools also capture different CNVs. Further improvements in precision requires additional development of tools, reference data sets, and annotation of CNVs, potentially assisted by the use of background panels for filtering of frequently called variants.

bioinformatics↗

bioRxiv2021

Spectracular: minimizing spectral overlap in multicolor flow cytometry experiments

Selecting fluorochromes for polychromatic panels for flow cytometry is complex and time-consuming. Poorly designed panels can result in overlap between the emission spectra of the different fluorochromes, making their signals difficult to separate. While assessing all possible panels is simple to do programmatically, the combinatorial complexity of most real world problems renders brute force computation impractical. Here we present a novel complexity optimization algorithm for fast design of minimal spectral overlap fluorochrome combinations. To aid researchers in designing fluorochrome panels we implemented the algorithm in a web server, Spectracular, that also considers instrument laser configuration and allows users to define various constraints related to antibody-fluorochrome availability and marker co-expression.

bioinformatics↗

bioRxiv2020

Single cell transcriptional characterization of human megakaryocyte lineage commitment and maturation

In the current understanding of adult bone marrow hematopoiesis, megakaryocytes (MKs) originate from cells immuno-phenotypically indistinguishable from hematopoietic stem cells (HSCs), bypassing intermediate progenitors. Here, we use single cell RNA sequencing to characterize HSCs and MKs from human bone marrow, to investigate MK lineage commitment and maturation. We identify two MK primed HSC clusters exhibiting unique differentiation kinetics, at least one of which is used in steady state and stress thrombopoiesis. By analyzing transcriptional signatures we show that human bone marrow MKs originate from MK primed HSC subpopulations, supporting the notion that these display exclusive priming for MK differentiation. We show that transcriptional programs change with increasing MK ploidy, where genes upregulated in high ploidy states may have functional relevance in platelet production. Finally, we highlight the presence of a specific transcriptional signature in MKs from individuals with myocardial infarction, supporting the aberration of MK differentiation in this thrombotic state.

cell biology↗

bioRxiv2019

Semaphorin 3A induces cytoskeletal paralysis in tumor-specific CD8+ T cells

Semaphorin-3A (Sema3A) regulates tumor angiogenesis, but its role in modulating anti-tumor immunity is unclear. We demonstrate that Sema3A secreted within the tumor microenvironment (TME) suppresses tumor-specific CD8+ T cell function via Neuropilin-1 (NRP1), a receptor that is upregulated upon activation with T cells cognate antigen. Sema3A inhibits T cell migration, assembly of the immunological synapse, and tumor killing. It achieves these functional effects through hyper-activating the acto-myosin system in T cells leading to cellular paralysis. Finally, using a clear cell renal cell carcinoma patient cohort, we demonstrate that human tumor-specific CD8+ T cells express NRP1 and are trapped in Sema3A rich regions of tumors. Our study establishes Sema3A as a potent inhibitor of anti-tumor immunity.

immunology↗