A5075215786- Single-cell and spatial transcriptomics
- Epigenetics and DNA Methylation
- Extracellular vesicles in disease
- Immune Cell Function and Interaction
- T-cell and B-cell Immunology
- Cell Image Analysis Techniques
- Chronic Lymphocytic Leukemia Research
- Immune cells in cancer
- Monoclonal and Polyclonal Antibodies Research
- Phagocytosis and Immune Regulation
- Hematopoietic Stem Cell Transplantation
- Cancer-related molecular mechanisms research
- Genomics and Rare Diseases
- Genomic variations and chromosomal abnormalities
- Cancer Genomics and Diagnostics
- Cancer Immunotherapy and Biomarkers
- Hematological disorders and diagnostics
- Gene Regulatory Network Analysis
- Multiple Myeloma Research and Treatments
- Family Support in Illness
- Artificial Intelligence in Healthcare
- Lymphoma Diagnosis and Treatment
- Rheumatoid Arthritis Research and Therapies
- Cancer Mechanisms and Therapy
- Molecular Biology Techniques and Applications
University of Helsinki
2024-2025
Åbo Akademi University
2018-2024
Turku Centre for Computer Science
2019-2024
University of Turku
2018-2024
Helsinki University Hospital
2024
Tampere University
2019
Turku Centre for Biotechnology
2018-2019
Abstract Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors are commonly used to treat non-small cell lung cancers with EGFR mutations, but drug resistance often emerges. Intratumor heterogeneity is a known cause of targeted therapy and considered major in treatment failure. This study identifies clones EGFR-mutant tumors expressing low levels both wild-type mutant protein. These EGFR-low cells intrinsically more tolerant inhibitors, invasive, exhibit an...
Abstract Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments investigate cell-type-specific differential states (DS) conditions. This can be performed by first identifying the cell types in all subjects then performing a DS analysis conditions within each type. Naïve single-cell methods that treat...
Alzheimer's disease results from a neurodegenerative process that starts well before the diagnosis can be made. New prognostic or diagnostic markers enabling early intervention into would highly valuable. Environmental and lifestyle factors largely modulate risk may influence pathogenesis through epigenetic mechanisms, such as DNA methylation. As environmental affect multiple tissues of body, we hypothesized disease-associated methylation signatures are detectable in peripheral blood...
Genomics data provide great opportunities for translational research and the clinical practice, example, predicting disease stages. However, classification of such is a challenging task due to their high dimensionality, noise, heterogeneity. In recent years, deep learning classifiers generated much interest, but complexity, so far, little known about utility this method genomics. paper, we address problem by studying computational diagnostics breast cancer inflammatory bowel patients based...
The serine/threonine-specific Moloney murine leukemia virus (PIM) kinase family (i.e., PIM1, PIM2, and PIM3) has been extensively studied in tumorigenesis. PIM kinases are downstream of several cytokine signaling pathways that drive immune-mediated diseases. Uncontrolled T helper 17 (Th17) cell activation associated with the pathogenesis autoimmunity. However, detailed molecular function PIMs human Th17 regulation yet to be studied. In present study, we comprehensively investigated how three...
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 types. Here, we introduce Coralysis, an R package featuring a multi-level algorithm to overcome these challenges. Coralysis enables sensitive integration, reference-mapping, and state identification demonstrating consistent performance diverse RNA-seq tasks outperforming are unevenly...
Abstract Immunotherapy has transformed cancer treatment, offering promising outcomes even in previously incurable cancers. However, responses to immuno-oncology (IO) treatments remain limited, with only a fraction of patients benefiting from these therapies. This limited efficacy arises lack predictive biomarkers, unknown mechanisms immune resistance, and the complex tumor-immune interactions, making it challenging predict which will benefit costly treatments. To address issues, we developed...
Detection of copy number variations (CNVs) from high-throughput next-generation whole-genome sequencing (WGS) data has become a widely used research method during the recent years. However, only little is known about applicability developed algorithms to ultra-low-coverage (0.0005-0.8×) that in various and clinical applications, such as digital karyotyping single-cell CNV detection.
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 methods and picking giving most biologically sensible model. As default parameters are often suboptimal, their tuning requires methodological expertise.We...
Gene regulatory elements, such as enhancers, greatly influence cell identity by tuning the transcriptional activity of specific types. Dynamics enhancer landscape during early human Th17 differentiation remains incompletely understood. Leveraging ATAC-seq-based profiling chromatin accessibility and comprehensive analysis key histone marks, we identified a repertoire enhancers that potentially exert control over fate specification cells. We found 23 SNPs associated with autoimmune diseases...
Somatic mutations in T cells can cause cancer but also have implications for immunological diseases and cell therapies. The mutation spectrum nonmalignant is unclear. Here, we examined somatic CD4 + CD8 from 90 patients with hematological disorders used receptor (TCR) single-cell sequencing to link expansions phenotypes. had a higher burden than cells. Notably, the biggest variant allele frequency (VAF) of non-synonymous variants was synonymous cells, indicating non-random occurrence. VAF...
Despite immunotherapy’s promise in cancer treatment, patient responses vary substantially because of the individual nature immune system and lack reliable biomarkers. To address this issue, we developed a precision ex vivo platform that integrates patient-specific tumor cells to study mechanisms antitumor response, predict immunotherapy outcomes, identify effective treatments. This revealed unique single-cell response sensitivities standard-of-care immunotherapies. Furthermore, were able...
Rheumatoid arthritis (RA) is a complex autoimmune disease targeting synovial joints. Traditionally, RA divided into seropositive (SP) and seronegative (SN) forms, the latter consisting of an array unrelated diseases with joint involvement. Recently, we described severe form SN-RA that associates characteristic destruction. Here, sought biological characteristics to differentiate this rare but aggressive anti-citrullinated peptide antibody-negative destructive (CND-RA) from early (SP-RA)...
Abstract Motivation Computational models are needed to infer a representation of the cells, i.e. trajectory, from single-cell RNA-sequencing data that model cell differentiation during dynamic process. Although many trajectory inference methods exist, their performance varies greatly depending on dataset and hence there is need establish more accurate, better generalizable methods. Results We introduce scShaper, new method enables accurate linear inference. The ensemble approach scShaper...
Abstract Background Deciphering the meaning of human DNA is an outstanding goal which would revolutionize medicine and our way for treating diseases. In recent years, non-coding RNAs have attracted much attention shown to be functional in part. Yet importance these especially higher biological functions remains under investigation. Methods this paper, we analyze RNA-seq data, including protein coding RNAs, from lung adenocarcinoma patients, a histologic subtype non-small-cell cancer, with...
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 high dimensionality data makes their identification challenging.We introduce ILoReg, an R package implementing a new population method that improves with through probabilistic feature extraction step is applied before visualization. The performed using novel machine learning algorithm, called...
Abstract Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multi-subject, multi-condition experiments investigate cell-type-specific differential states (DS) conditions. This can be performed by first identifying the cell types in all subjects then performing a DS analysis conditions within each type. Naïve single-cell methods that...
ABSTRACT Alzheimer’s disease (AD) results from a neurodegenerative process that starts well before the diagnosis can be made. New prognostic or diagnostic markers enabling early intervention into would highly valuable. As life style factors largely modulate risk, we hypothesised associated DNA methylation signatures are detectable in peripheral blood of discordant twin pairs. Reduced Representation Bisulfite Sequencing, single cell RNA-sequencing and gene array data were utilised to examine...
Abstract 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 high dimensionality data makes their identification challenging. We introduce ILoReg ( https://github.com/elolab/iloreg ), an R package implementing a new population method that achieves differentiation resolution through probabilistic feature extraction step is applied before visualization.