Biology subjects

Pitkanen, E.

Publications and source records attributed to Pitkanen, E..

10 recordsLinked to original sources

bioRxiv2026

Classical enhancers couple cis-regulatory logic with transcriptional condensates and 3D genome architecture

Deciphering the regulatory logic of enhancers remains a central question in understanding cell- and tissue-specific gene expression in multicellular organisms. This is particularly pertinent at multipartite enhancer clusters such as super-enhancers, where multiple enhancers contribute to the expression of a single gene. Gene expression has been studied largely through sequence-dependent recruitment of transcription factors (TF) and co-activators, whereas 3D chromatin structure has been attributed to architectural proteins such as cohesion and CTCF1-3. However, the contribution of DNA sequence encoded in enhancers to shaping higher-order genome organization remains poorly understood. Here we show that classical enhancers, embedded within multipartite super-enhancer structures, act as determinant regulatory elements that initiate the gene regulatory cascade by linking DNA sequence recognition to 3D chromatin architecture. Classical enhancers are more evolutionarily conserved and display stronger regulatory activity than facilitator elements, which lack intrinsic enhancer activity but potentiate classical enhancer function. We show that classical enhancers are selectively bound by specific TFs with strong intrinsically disordered regions, such as NFE2L2 in liver cancer cells, capable of driving transcriptional condensate formation through phase separation. NFE2L2 depletion reduced enhancer activity and induced widespread chromatin reorganization, characterized by increased cohesin and CTCF occupancy at super-enhancer boundaries and beyond. This "cohesin clogging" impaired DNA loop extrusion, led to formation of smaller topologically associated domains, and weakened enhancer-promoter contacts. These findings highlight that sequence-specific TFs have multifaceted roles beyond transcriptional control, establishing a direct mechanistic link between enhancer sequence, TF binding, condensate formation, and 3D genome organization, with the regulatory logic being encoded in the DNA sequence itself.

genomics↗

bioRxiv2025

Compound-specific DNA adduct profiling with nanopore sequencing and IonStats

AO_SCPLOWBSTRACTC_SCPLOWCovalently bound DNA adducts are mutation precursors that contribute to aging and diseases such as cancer. Accurate detection of adducts in the genome will shed light on tumorigenesis. Commonly used detection methods are unable to pinpoint the exact genomic locations of adducts. Long-read nanopore sequencing has the potential to accurately detect multiple types of DNA adducts at single-nucleotide precision. In this study, we developed a novel statistical toolkit, IonStats, to profile DNA adducts in nanopore sequencing data. With IonStats, we investigated the effects of four adduct-inducing genotoxic compounds on nanopore sequencing, and found both shared and compound-specific perturbations in base quality scores, ionic current profiles, and translocation dynamics. Notably, aristolochic acid II and melphalan treatments profoundly altered nanopore readouts and led to substantial sequence-specific read interruptions. Our study shows that nanopore sequencing can be effectively employed to detect and characterize DNA adducts, paving the way for high-resolution, high-throughput profiling of DNA damage and the exposome.

bioinformatics↗

bioRxiv2025

Blood donor biobank pipeline to collect genome-based samples for research

The integration of genome data with electronic health records, driven by large biobank studies, has advanced human genetics by allowing systematic exploration of genotype-phenotype links. Regular donation enables large, longitudinal sample cohorts. Because blood donors are generally healthy, disease treatments or progression do not disturb interpretations in functional studies. We describe here a pipeline on how to collect blood donors high quality plasma, serum, and living cell samples for multi-omics studies. Peripheral blood mononuclear cells (PBMC) were frozen and, after thawing, contained standard levels of immune cell subpopulations, responded to immune activation, and were of good quality starting material for multi-omics and cell imaging studies. We demonstrate that most genetic variants of interest to the major genomics study in Finland, FinnGen, could be found by random collection of samples during the standard blood donation without recall. Probing simple associations in the multi-omics data confirmed expected associations with e.g. age and sex, demonstrating good sample quality. As an example of interesting findings, we observed a significant association between frequent blood donation and lower levels of per- and polyfluoroalkyl substances (PFAS). The study demonstrates that regular blood donors are a suitable target population for high-quality, cost-effective sample collections.

molecular biology↗

bioRxiv2025

Whole Exome Sequencing and Single-Cell DNA Sequencing for Assessment of Clonal Heterogeneity and Evolution in Acute Myeloid Leukemia

BackgroundAcute myeloid leukemia (AML) progresses by the accumulation of somatic mutations and clonal expansion of pre-leukemic cells. Patients may respond to initial therapy, but often relapse, underscoring an evolving disease. Next generation sequencing technologies are being applied to AML for risk stratification and monitoring treatment response. We aimed to evaluate the efficiency of whole exome sequencing (WES) and single-cell DNA sequencing (scDNA-seq) for determining clonal heterogeneity and evolution in AML induced by treatment, assessing strengths and limitations of each technology. MethodsWe conducted WES and scDNA-seq on samples from 6 patients with AML, including sequential samples from four patients. We identified somatic variants, clonal composition and phylogeny using both technologies and compared the results. ResultsWES detected more variants and clones due to broader coverage, while scDNA-seq provided clonality results for targeted genes revealing zygosity and rare clones. Both techniques missed clinically important variants, posing challenges for clinical application. However, they identified similar founding clones and strong correlation of variant allele frequencies and clonal prevalences. ConclusionsAs both technologies can overlook variants, multiple technologies should be utilized to understand clonality in heterogeneous diseases such as AML. Careful scDNA-seq target panel planning, utilizing knowledge obtained from bulk sequencing, can offer more information on clonal heterogeneity. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/656982v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1e429b1org.highwire.dtl.DTLVardef@6d650forg.highwire.dtl.DTLVardef@5eb31org.highwire.dtl.DTLVardef@93e735_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2024

Morphological single-cell analysis of peripheral blood mononuclear cells from 390 healthy blood donors with Blood Cell Painting

The morphological diversity of blood immune cells of healthy individuals, critical for recognizing disease-related phenotypes, remains largely uncharacterized. To address this gap, we developed Blood Cell Painting (BCP): a high content, high throughput fluorescence imaging assay for peripheral blood mononuclear cells. We generated a BCP Atlas with images of 50 million cells from 390 healthy blood donors, identifying 18 distinct immune cell morphology clusters. A genome-wide association study of BCP-derived imaging-based cellular features revealed 93 significant associations across 30 genetic loci. These loci include genes linked to mast cell function, inflammation, immune signaling, mitochondrial maintenance and circadian immune modulation. We also observed correlations between immune cell morphological features and clinical traits, such as respiratory conditions and healthcare visits related to contraceptive management, potentially reflecting hormonal influences on immune cell phenotypes. As a proof of concept for clinical application, acute myeloid leukemia subtypes were distinguished by BCP. Our study establishes BCP as a versatile method for immune cell profiling to uncover genetic, phenotypic and clinical determinants of immune cell morphology in health and disease.

cell biology↗

bioRxiv2023

ArcheD, a residual neural network for prediction of cerebrospinal fluid amyloid-beta from amyloid PET images

Detection and measurement of amyloid-beta (A{beta}) aggregation in the brain is a key factor for early identification and diagnosis of Alzheimers disease (AD). We aimed to develop a deep learning model to predict A{beta} cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 A{beta} PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical A{beta}. We also compared the brain regions relevance for the models CSF prediction within clinical-based and biological-based classifications. ArcheD-predicted A{beta} CSF values correlated strongly with measured A{beta} CSF values (r=0.81; p<0.001) and showed correlations with SUVR and episodic memory measures in all participants except in those with AD. For both clinical and biological classifications, cerebral white matter significantly contributed to CSF prediction (q<0.01), specifically in non-symptomatic and early stages of AD. However, in late-stage disease, brain stem, subcortical areas, cortical lobes, limbic lobe, and basal forebrain made more significant contributions (q<0.01). Considering cortical gray matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted A{beta} CSF concentration from A{beta} PET scans, offering potential clinical utility for A{beta} level determination and early AD detection.

neuroscience↗

bioRxiv2022

DeepIFC: virtual fluorescent labeling of blood cells in imaging flow cytometry data with deep learning

Imaging flow cytometry (IFC) combines flow cytometry with microscopy, allowing rapid characterization of cellular and molecular properties via high-throughput single-cell fluorescent imaging. However, fluorescent labeling is costly and time-consuming. We present a computational method called DeepIFC based on the Inception U-Net neural network architecture, able to generate fluorescent marker images and learn morphological features from IFC brightfield and darkfield images. Furthermore, the DeepIFC workflow identifies cell types from the generated fluorescent images and visualizes the single-cell features generated in a 2D space. We demonstrate that rarer cell types are predicted well when a balanced data set is used to train the model, and the model is able to recognize red blood cells not seen during model training as a distinct entity. In summary, DeepIFC allows accurate cell reconstruction, typing and recognition of unseen cell types from brightfield and darkfield images via virtual fluorescent labeling.

bioinformatics↗

bioRxiv2022

Mutation-Attention (MuAt): deep representation learning of somatic mutations for tumour typing and subtyping

Cancer genome sequencing enables accurate classification of tumours and tumour sub-types. However, prediction performance is still limited using exome-only sequencing and for tumor types with low somatic mutation burden such as many pediatric tumours. Moreover, the ability to leverage deep representation learning in discovery of tumour entities remains unknown. We introduce here Mutation-Attention (MuAt), a deep neural network to learn representations of simple and complex somatic alterations for prediction of tumour types and subtypes. MuAt achieved prediction accuracy of 89% for whole genomes (24 tumour types) and 64% for whole exomes (20 types), and a top-5 accuracy of 97% and 90%, respectively. Tumour representations learnt by MuAt included tumour entities such as acral melanoma, SHH-activated medulloblastoma, SPOP-associated prostate cancer, microsatellite instability, and MUTYH-associated pancreatic endocrine tumours although these tumour subtypes and subgroups were not used as training labels. Integrated representations of somatic alterations hold significant potential to drive discovery of novel tumour entities and clinical application.

genomics↗

bioRxiv2017

Germline determinants of the somatic mutation landscape in 2,642 cancer genomes

Cancers develop through somatic mutagenesis, however germline genetic variation can markedly contribute to tumorigenesis via diverse mechanisms. We discovered and phased 88 million germline single nucleotide variants, short insertions/deletions, and large structural variants in whole genomes from 2,642 cancer patients, and employed this genomic resource to study genetic determinants of somatic mutagenesis across 39 cancer types. Our analyses implicate damaging germline variants in a variety of cancer predisposition and DNA damage response genes with specific somatic mutation patterns. Mutations in the MBD4 DNA glycosylase gene showed association with elevated C>T mutagenesis at CpG dinucleotides, a ubiquitous mutational process acting across tissues. Analysis of somatic structural variation exposed complex rearrangement patterns, involving cycles of templated insertions and tandem duplications, in BRCA1-deficient tumours. Genome-wide association analysis implicated common genetic variation at the APOBEC3 gene cluster with reduced basal levels of somatic mutagenesis attributable to APOBEC cytidine deaminases across cancer types. We further inferred over a hundred polymorphic L1/LINE elements with somatic retrotransposition activity in cancer. Our study highlights the major impact of rare and common germline variants on mutational landscapes in cancer.

genomics↗

bioRxiv2017

BasePlayer: Versatile Analysis Software For Large-Scale Genomic Variant Discovery

Next-generation sequencing (NGS) is being routinely applied in life sciences and clinical practice, where the interpretation of the resulting massive data has become a critical challenge. Computational workflows, such as the Broad GATK, have been established to take raw sequencing data and produce processed data for downstream analyses. Consequently, results of these computationally demanding workflows, consisting of e.g. sequence alignment and variant calling, are increasingly being provided for customers by sequencing and bioinformatics facilities. However, downstream variant analysis, whole-genome level in particular, has been lacking a multi-purpose tool, which could take advantage of rapidly growing genomic information and integrate genetic variant, sequence, genomic annotation and regulatory (e.g. ENCODE) data interactively and in a visual fashion. Here we introduce a highly efficient and user-friendly software, BasePlayer (http://baseplayer.fi), for biological discovery in large-scale NGS data. BasePlayer enables tightly integrated comparative variant analysis and visualization of thousands of NGS data samples and millions of variants, with numerous applications in disease, regulatory and population genomics. Although BasePlayer has been designed primarily for whole-genome and exome sequencing data, it is well-suited to various study settings, diseases and organisms by supporting standard and upcoming file formats. BasePlayer transforms an ordinary desktop computer into a large-scale genomic research platform, enabling also a non-technical user to perform complex comparative variant analyses, population frequency filtering and genome level annotations under intuitive, scalable and highly-responsive user interface to facilitate everyday genetic research as well as the search of novel discoveries.

bioinformatics↗