Biology subjects

Elo, L. L.

Publications and source records attributed to Elo, L. L..

At least 19 recordsLinked to original sources

bioRxiv2026

Genetic Disruption at the CIP2A Locus Modulates T Cell Responses and Attenuates Experimental Autoimmune Encephalomyelitis

Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system (CNS) driven by pathogenic T cell-mediated inflammation. Fingolimod (FTY720), an approved therapy for MS, is an established activator of protein phosphatase 2A (PP2A). However the contribution of PP2A in autoimmune neuroinflammation remains incompletely understood. Here, we addressed this question using experimental autoimmune encephalomyelitis (EAE), a murine model of MS, in mice carrying a genetic disruption of the locus encoding cancerous inhibitor of protein phosphatase 2A (CIP2A), an endogenous inhibitor of PP2A. Mice with disruption of the CIP2A locus, the knock out (KO) mice, exhibited attenuated EAE severity compared with wild-type (WT) controls. Histological and flow-cytometric analyses revealed markedly reduced infiltration of mononuclear cells, including CD4 and CD4CXCR6 encephalitogenic T cells, in the CNS of diseased KO mice. Reduced numbers of these T cell populations were also observed in peripheral lymphoid organs of the Cip2a-deficient mice during EAE, while T cell abundance was comparable under steady-state conditions, suggesting impaired activation-induced expansion rather than altered homeostasis or migration. Single-cell RNA sequencing of CNS and lymph node immune cells revealed changes in cell-type abundance and gene expression. Notably, Il17a expression was reduced in CNS CD8+ T cells and showed a similar trend in {gamma}{delta} T cells. Together, our findings reveal that genetic disruption at the CIP2A locus attenuates EAE, possibly by limiting the expansion and accumulation of encephalitogenic T cell populations in CNS. These results identify the CIP2A locus as a previously unrecognized regulator of T cell-driven autoimmune neuroinflammation and provide new insights into mechanisms that restrain pathogenic T cell responses during EAE.

immunology↗

bioRxiv2026

ROTS 2.0: A reproducibility-driven framework for robust statistical modeling across diverse high-throughput omics study designs

Reproducibility is fundamental to reliable scientific discoveries. The reproducibility-optimized test statistic (ROTS) is a robust framework designed to identify reproducible features (e.g. genes or proteins) in high-dimensional differential expression analyses such as transcriptomics and proteomics. This is achieved by optimizing the reproducibility of feature rankings under resampling. While originally implemented for univariate settings, ROTS now accommodates multi-group comparisons, survival analysis, linear models, and linear mixed-effects models, broadening its applicability to more complex and clinically relevant experimental designs. Using diverse simulations, benchmark datasets, and real-world case studies, we demonstrate the benefits of ROTS reproducibility optimization compared to the corresponding conventional test statistics. Additionally, we illustrate the utility of the reproducibility characteristics in assessing the overall reliability of the results. To facilitate widespread adoption, ROTS is provided as an open-source software package available through R/Bioconductor. Furthermore, to broaden the user base, we now also provide a Python interface available at pypi.org/project/PyROTS/.

bioinformatics↗

bioRxiv2025

Limitations of de novo sequencing in resolving sequence ambiguity

De novo peptide sequencing enables peptide identification from fragmentation spectra without relying on sequence databases. However, incomplete spectra create ambiguity, making unambiguous identification challenging. Recent deep learning advances have produced numerous de novo models that predict sequences and refine peptide-spectrum matches under such conditions. Yet, their relative strengths, weaknesses, and ability to handle spectrum ambiguity remain unclear. Here, we benchmark eight state-of-the-art models on three publicly available proteomics datasets, comparing performance using established metrics and quantifying inter-model agreement. We assess post-processing approaches, including iterative refinement, rescoring, and reranking, for their ability to improve identification accuracy, and perform an error analysis to identify common mispredictions and their causes. Model performance varied, with considerable overlap of correct identifications. Post-processing yielded no or only modest improvements. Most sequencing errors were model-independent and driven by limited fragment ion coverage, a limitation also observed in database searches with large search spaces.

bioinformatics↗

bioRxiv2025

A Metabolite-Based Resistance Mechanism Against Malaria

Whether jaundice, a common presentation of Plasmodium (P.) falciparum malaria (1-3) arising from the accumulation of circulating bilirubin, represents an adaptive or maladaptive response to Plasmodium spp. infection is not understood (1-3). We found that asymptomatic P. falciparum infection was associated with a >10-fold higher ratio of unconjugated bilirubin over parasite burden, compared to symptomatic malaria. Genetic suppression of bilirubin synthesis by biliverdin reductase A (BVRA) (4) increased parasite virulence and malaria mortality in mice. Accumulation of unconjugated bilirubin in plasma, via genetic inhibition of hepatic conjugation by UDP glucuronosyltransferase family 1 member A1 (UGT1A1) (5), was protective against malaria in mice. Unconjugated bilirubin inhibited P. falciparum proliferation in red blood cells (RBC) via a mechanism that suppressed mitochondrial pyrimidine synthesis. Moreover, unconjugated bilirubin inhibited hemozoin (Hz) crystallization and compromised the parasites food vacuole. In conclusion, jaundice represents a metabolic response to Plasmodium spp. infection that limits malaria severity.

immunology↗

bioRxiv2025

Testosterone exposure during fetal masculinization programming window determines the kidney size in adult mice.

Kidney size is sex-dimorphic and regulated by androgens in adult humans and mice. However, the effects of developmental androgen deficiency on kidneys remain elusive. We hypothesized that androgens program future kidney growth during a fetal development. Male mice lacking main testosterone-producing enzyme HSD17B3 had reduced testosterone at embryonic day 15.5, but the concentrations increased by E18.5, creating a short time window of androgen deficiency resulting in reduced kidney size in adult males. In male Hsd17b3-/-kidneys, nephron development was qualitatively normal, but number of glomeruli and proliferation of proximal tubules was reduced. Testosterone supplementation at E14.5-17.5 normalized the renal size in adult Hsd17b3-/-males. Our data suggests that androgen receptor and Hnf4a jointly regulate Igfbp5, FOXO1 and mTOR signaling to convey male-specific kidney growth. In conclusion, we have identified a novel developmental programming effect on male kidneys, where fetal androgen deficiency reduces kidney growth and androgen responsiveness in adult males.

developmental biology↗

bioRxiv2025

Coralysis enables sensitive identification of imbalanced cell types and states in single-cell data via multi-level integration

Current state-of-the-art integration methods for single-cell transcriptomics often struggle with imbalanced cell types across heterogeneous datasets, particularly when the datasets include similar but unshared cell types. Here, we introduce Coralysis, an R package featuring a multi-level integration algorithm to overcome these challenges. Coralysis enables sensitive integration, reference-mapping, and cell state identification across single-cell datasets, demonstrating consistent performance across diverse single-cell RNA-seq integration tasks and outperforming state-of-the-art methods when similar cell types are unevenly distributed across batches or completely absent from some datasets. Beyond single-cell transcriptomics, Coralysis enables the integration of rare cell populations from single-cell proteomic assays, such as basophils (0.5%) from whole blood. It also accurately predicts cell type identities across various query-reference scenarios. For instance, it successfully reclassifies CD16+ monocytes and natural killer cells that were previously misclassified as CD14+ monocytes and cytotoxic T cells in peripheral blood mononuclear cells. Finally, a key feature of Coralysis is its ability to provide probability scores that enable identifying both transient and steady cell states along with their differential expression programs. Overall, Coralysis facilitates the study of subtle biological variation and its dynamics by improving the integration of imbalanced cell types and states, enabling a more faithful representation of the cellular landscape in complex single-cell experiments.

bioinformatics↗

bioRxiv2022

Totem: a user-friendly tool for clustering-based inference of tree-shaped trajectories from single-cell data

Single-cell RNA-sequencing enables cell-level investigation of cell differentiation, which can be modelled using trajectory inference methods. While tremendous effort has been put into designing these methods, inferring accurate trajectories automatically remains difficult. Therefore, the standard approach involves testing different trajectory inference methods and picking the trajectory giving the most biologically sensible model. As the default parameters are often suboptimal, their tuning requires methodological expertise. We introduce Totem, an open-source, easy-to-use R package designed to facilitate inference of tree-shaped trajectories from single-cell data. Totem generates a large number of clustering results, estimates their topologies as minimum spanning trees, and uses them to measure the connectivity of the cells. Besides automatic selection of an appropriate trajectory, cell connectivity enables to visually pinpoint branching points and milestones relevant to the trajectory. Furthermore, testing different trajectories with Totem is fast, easy, and does not require in-depth methodological knowledge.

bioinformatics↗

bioRxiv2022

Longitudinal pathway analysis using structural information with case studies in early type 1 diabetes

We introduce a new method for Pathway Analysis of Longitudinal data (PAL), which is suitable for complex study designs, such as longitudinal data. The main advantages of PAL are the use of pathway structures and the suitability of the approach for study settings beyond currently available tools. We demonstrate the performance of PAL with three longitudinal datasets related to the early development of type 1 diabetes, involving different study designs and only subtle biological signals. Transcriptomic and proteomic data are represented among the test data. An R package implementing PAL is publicly available at https://github.com/elolab/PAL. MotivationPathway analysis is a frequent step in studies involving gene or protein expression data, but most of the available pathway methods are designed for simple case versus control studies of two sample groups without further complexity. The few available methods allowing the pathway analysis of more complex study designs cannot use pathway structures or handle the situation where the variable of interest is not defined for all samples. Such scenarios are common in longitudinal studies with so long follow up time that healthy controls are required to identify the effect of normal aging apart from the effect of disease development, which is not defined for controls. PAL is the first available pathway method to analyse such high-investment datasets.

bioinformatics↗

bioRxiv2022

Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multi-subject, multi-condition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first identifying the cell types in all the subjects and then by performing a DS analysis between the conditions within each cell type. Naive single-cell DS analysis methods that treat cells statistically independent are subject to false positives in the presence of variation between biological replicates, an issue known as the pseudo-replicate bias. While several methods have already been introduced to carry out the statistical testing in multi-subject scRNA-seq analysis, comparisons that include all these methods are currently lacking. Here, we performed a comprehensive comparison of 18 methods for the identification of DS changes between conditions from multi-subject scRNA-seq data. Our results suggest that the pseudo-bulk methods performed generally best. Both pseudo-bulks and mixed models that model the subjects as a random effect were superior compared with the naive single-cell methods that do not model the subjects in any way. While the naive models achieved higher sensitivity than the pseudo-bulk methods and the mixed models, they were subject to a high number of false positives. In addition, accounting for subjects through latent variable modeling did not improve the performance of the naive methods.

bioinformatics↗

bioRxiv2021

Myosin-X-dependent assembly of the extracellular matrix limits breast cancer invasion

Ductal carcinoma in situ (DCIS) is a pre-invasive stage of breast cancer, where the tumor is encapsulated by a basement membrane (BM). At the invasive phase, the BM barrier is compromised enabling tumor cells to escape into the surrounding stroma. The molecular mechanisms that establish and maintain an epithelial BM barrier in vivo are poorly understood. Myosin-X (MYO10) is a filopodia-inducing motor protein implicated in metastasis and poor clinical outcome in patients with invasive breast cancer (IBC). We compared MYO10 expression in patient-matched normal breast tissue and DCIS lesions and found elevated MYO10 expression in DCIS samples, suggesting that MYO10 might facilitate the transition from DCIS to IBC. Indeed, MYO10 promoted the formation of filopodia and cell invasion in vitro and positively regulated the dissemination of individual cancer cells from IBC lesions in vivo. However, MYO10-depleted DCIS xenografts were, unexpectedly, more invasive. In these xenografts, MYO10 depletion compromised BM formation around the lesions resulting in poorly defined tumor borders and increased cancer cell dispersal into the surrounding stroma. Moreover, MYO10-depleted tumors showed increased EMT-marker-positive cells, specifically at the tumor periphery. We also observed cancer spheroids undergoing rotational motion and recruiting BM components in a filopodia-dependent manner to generate a near-continuous extracellular matrix boundary. Taken together, our data identify a protective role for MYO10 in early-stage breast cancer, where MYO10-dependent tumor cell protrusions support BM assembly at the tumor-stroma interface to limit cancer progression, and a pro-invasive role that facilitates cancer cell dissemination at later stages. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/464987v2_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@d90a4eorg.highwire.dtl.DTLVardef@133ba9borg.highwire.dtl.DTLVardef@1204a05org.highwire.dtl.DTLVardef@dba254_HPS_FORMAT_FIGEXP M_FIG C_FIG Highlights- Filopodia sculpt the tumor-proximal stroma in pre-invasive ductal carcinoma in situ (DCIS). - Filopodia-dependent basement membrane (BM) assembly limits invasive transition of DCIS-like tumors in vivo. - Loss of MYO10-dependent filopodia impairs BM assembly and induces an EMT-like phenotype at the tumor-stroma interface in vivo. - MYO10 filopodia are anti-invasive in DCIS but facilitate dissemination in invasive breast cancer.

cell biology↗

bioRxiv2021

PhosPiR: An automated phospho-proteomic pipeline in R

Large-scale phospho-proteome profiling using mass spectrometry (MS) provides functional insight that is crucial for disease biology and drug discovery. However, extracting biological understanding from this data is an arduous task requiring multiple analysis platforms that are not adapted for automated high-dimensional data analysis. Here, we introduce an integrated pipeline that combines several R packages to extract high-level biological understanding from largescale phosphoproteomic data by seamless integration with existing databases and knowledge resources. In a single run, PhosPiR provides data clean-up, fast data overview, multiple statistical testing, differential expression analysis, phospho-site annotation and translation across species, multi-level enrichment analyses, proteome-wide kinase activity and substrate mapping and network hub analysis. Data output includes graphical formats such as heatmap, box-, volcano- and circos-plots. This resource is designed to assist proteome-wide data mining of pathophysiological mechanism without a need for programming knowledge.

bioinformatics↗

bioRxiv2021

Variant calling tool evaluation for variable size indel calling from next generation whole genome and targeted sequencing data

Insertions and deletions (indels) in human genomes are associated with a wide range of phenotypes, including various clinical disorders. High-throughput, next generation sequencing (NGS) technologies enable detection of short genetic variants, such as single nucleotide variants (SNVs) and indels. However, the variant calling accuracy for indels remains considerably lower than for SNVs. Here we present a comparative study of the performance of variant calling tools on indel calling, evaluated with a wide repertoire of NGS datasets. While there is no single optimal tool to suit all circumstances, our results demonstrate that the choice of variant calling tool greatly impacts the precision and recall of indel calling. Furthermore, to reliably detect indels, it is essential to choose NGS technologies that offer a long read length and high coverage, coupled with specific variant calling tools. Author summaryThe development of next generation sequencing (NGS) technologies and computational algorithms enabled large scale, simultaneous detection of wide range of genetic variants, such as single nucleotide variants as well as insertions and deletions (indels), which may confer potential clinical significance. Recently, many studies have been conducted to evaluate variant calling tools on indel calling. However, the optimal indel size range for different variant calling tools remain unclear. A good benchmarking dataset for indel calling evaluation should contain biologically representative high-confident indels with a wide size range and preferably come from various sequencing settings. In this article, we created a semi-simulated whole genome sequencing dataset where the sequencing data was computationally generated. The indels in the semi-simulated genome were incorporated from a real human sample to represent biologically realistic indels and to avoid inclusion of variants due to potential technical sequencing errors. Furthermore, we used three real-world NGS datasets generated by whole genome or targeted sequencing to further evaluate our candidate tools. Our results demonstrated that variant calling tools varies greatly in calling different sizes of indels. Deletion calling and insertion calling also showed differences among the tools. The sequencing settings in coverage and read length also had a great impact on indel calling. Our results suggest that the accurate indel calling was dependent on the combination of a variant calling tool, indel size range and sequencing settings.

bioinformatics↗

bioRxiv2021

scShaper: ensemble method for fast and accurate linear trajectory inference from single-cell RNA-seq data

Computational models are needed to infer a representation of the cells, i.e. a trajectory, from single-cell RNA-sequencing data that model cell differentiation during a dynamic process. Although many trajectory inference methods exist, their performance varies greatly depending on the dataset and hence there is a need to establish more accurate, better generalizable methods. We introduce scShaper, a new trajectory inference method that enables accurate linear trajectory inference. The ensemble approach of scShaper generates a continuous smooth pseudotime based on a set of discrete pseudotimes. We demonstrate that scShaper is able to infer accurate trajectories for a variety of nonlinear mathematical trajectories, including many for which the commonly used principal curves method fails. A comprehensive benchmarking with state-of-the-art methods revealed that scShaper achieved superior accuracy of the cell ordering and, in particular, the differentially expressed genes. Moreover, scShaper is a fast method with few hyperparameters, making it a promising alternative to the principal curves method for linear pseudotemporal ordering. scShaper is available as an R package at https://github.com/elolab/scshaper.

bioinformatics↗

bioRxiv2021

The AP-1 factors FOSL1 and FOSL2 co-regulate human Th17 responses

Th17 cells protect mucosal barriers, but their aberrant activity can cause autoimmunity. Molecular networks dictating human Th17 function are largely unexplored, and this hinders disease-studies. Here, we investigated the roles of the AP-1 factors, FOSL1 and FOSL2, in inducing human Th17 responses. Transient knockdown and over-expression strategies found the two proteins to inhibit Th17-cell identity, while revealing a distinct cooperativity between their functions. Strikingly, FOSL1 plays different roles in human and mouse and FOSL-mediated Th17 regulation is opposed by the AP-1 factor, BATF. Genome-wide occupancy analysis demonstrated the co-localization of FOSL1, FOSL2 and BATF in the vicinity of key Th17 genes. The functional interplay among these factors is possibly governed by sharing interactions with a common set of lineage-associated proteins. We further discovered that the genomic binding sites of these factors harbour a large number of disease-linked SNPs, many of which alter the ability of a given factor to bind DNA. Our findings thus provide crucial insights into the transcriptional regulation of human Th17 function and associated pathologies. ONE SENTENCE SUMMARYFOSL1- and FOSL2-mediated transcription during early human Th17 differentiation

immunology↗

bioRxiv2021

Enhanced longitudinal differential expression detection in proteomics with robust reproducibility optimization regression

Quantitative proteomics has matured into an established tool and longitudinal proteomic experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal omics data and introduce a new method, Robust longitudinal Differential Expression (RolDE). The methods were evaluated using nearly 2000 semi-simulated spike-in proteomic datasets and a large experimental dataset. The RolDE method performed overall best; it was most tolerant to missing values, displayed good reproducibility and was the top method in ranking the results in a biologically meaningful way. Furthermore, contrary to many approaches, the open source RolDE does not require prior knowledge concerning the types of differences searched, but can easily be applied even by non-experienced users.

bioinformatics↗

bioRxiv2020

Metaproteomics boosted up by untargeted data-independent acquisition data analysis framework

Mass spectrometry based metaproteomics is a relatively new field of research that provides the ability to characterize the functionality of microbiota. Recently, we were the first to demonstrate the applicability of data-independent acquisition (DIA) mass spectrometry to the analysis of complex metaproteomic samples. This allowed us to circumvent many of the drawbacks of the conventionally used data-dependent acquisition (DDA) mass spectrometry, mainly the limited reproducibility when analyzing samples with complex microbial composition. However, the previous method still required additional DDA data on the samples to assist the DIA analysis. Here, we introduce, for the first time, a DIA metaproteomics approach that does not require any DDA data, but instead replaces a spectral library generated from DDA data with a pseudospectral library generated directly from the metaproteomics DIA samples. We demonstrate that using the new DIA-only approach, we can achieve higher peptide yields than with the DDA-assisted approach, while the amount of required mass spectrometry data is reduced to a single DIA run per sample. The new DIA-only metaproteomics approach is implemented as open-source software package DIAtools 2.0, which is freely available from DockerHub.

bioinformatics↗

bioRxiv2020

ILoReg enables high-resolution cell population identification from single-cell RNA-seq data

Single-cell RNA-seq allows researchers to identify cell populations based on unsupervised clustering of the transcriptome. However, subpopulations can have only subtle transcriptomic differences and the high dimensionality of the data makes their identification challenging. We introduce ILoReg (https://github.com/elolab/iloreg), an R package implementing a new cell population identification method that achieves high differentiation resolution through a probabilistic feature extraction step that is applied before clustering and visualization.

bioinformatics↗

bioRxiv2020

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Mobile health, the collection of data using wearables and sensors, is a rapidly growing field in health research with many applications. Deriving validated measures of disease and severity that can be used clinically or as outcome measures in clinical trials, referred to as digital biomarkers, has proven difficult. In part due to the complicated analytical approaches necessary to develop these metrics. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of Parkinsons Disease (PD) and severity of three PD symptoms: tremor, dyskinesia and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC=0.87), as well as tremor (best AUPR=0.75), dyskinesia (best AUPR=0.48) and bradykinesia (best AUPR=0.95) severity.

bioengineering↗