Biology subjects

Partanen, J.

Publications and source records attributed to Partanen, J..

11 recordsLinked to original sources

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

Gene regulatory mechanisms guiding bifurcation of inhibitory and excitatory neuron lineages in the anterior brainstem

Selector transcription factors control choices of alternative cellular fates during development. The ventral rhombomere 1 of the embryonic brainstem produces neuronal precursors that can differentiate into either inhibitory GABAergic or excitatory glutamatergic neurons important for the control of behaviour. Transcription factors (TFs) Tal1, Gata2 and Gata3 are required for adopting the GABAergic neuronal identity and inhibiting the glutamatergic identity. Here, we asked how these selector TFs are activated and how they control the identity of the developing brainstem neurons. We addressed these questions by analysing chromatin accessibility at putative gene regulatory elements active during GABAergic and glutamatergic neuron lineage bifurcation, combined with studies of transcription factor expression and DNA-binding. Our results show that the Tal1, Gata2 and Gata3 genes are activated by highly similar mechanisms, with connections to regional patterning, neurogenic cell cycle exit and general course of neuronal differentiation. After activation, Tal1, Gata2 and Gata3 are linked by auto- and cross-regulation as well as regulatory interactions with transcription factors of the glutamatergic branch. Predicted targets of these selector TFs include genes expressed in GABAergic neurons, glutamatergic neurons, or both. Unlike genes specific to the glutamatergic branch, the genes expressed in GABAergic neurons appear to be under combinatorial control of Tal1, Gata2 and Gata3. Understanding gene regulatory interactions affecting the anterior brainstem GABAergic and glutamatergic neuron differentiation may give genetic and mechanistic insights into neurodevelopmental traits and disorders.

developmental biology↗

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

Accurate multi-population imputation of MICA, MICB, HLA-E, HLA-F and HLA-G alleles from genome SNP data

In addition to the classical HLA genes, the major histocompatibility complex (MHC) harbors a high number of other polymorphic genes with less established roles in disease associations and transplantation matching. To facilitate studies of the non-classical and non-HLA genes in large patient and biobank cohorts, we trained imputation models for MICA, MICB, HLA-E, HLA-F and HLA-G alleles on genome SNP array data. We show, using both population-specific and multi-population 1000 Genomes references, that the alleles of these genes can be accurately imputed for screening and research purposes. The best imputation model for MICA, MICB, HLA-E, -F and -G achieved a mean accuracy of 99.3% (min, max: 98.6, 99.9). Furthermore, validation of the 1000 Genomes exome short-read sequencing-based allele calling against a clinical-grade reference data showed an average accuracy of 99.8%, testifying for the quality of the 1000 Genomes data as an imputation reference. The imputation models, trained using the HIBAG algorithm, are available at GitHub (https://github.com/FRCBS/HLA_EFG_MICAB_imputation) and can be run locally, thus avoiding the need of sending sensitive genome data to remote portals.

bioinformatics↗

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

Gata2, Nkx2-2 and Skor2 form a transcription factor network regulating development of amidbrain GABAergic neuron subtype with characteristics of REM sleep regulatory neurons

The midbrain reticular formation is a mosaic of diverse GABAergic and glutamatergic neurons that have been associated with a variety of functions, including the regulation of sleep. However the molecular characteristics and development of the midbrain reticular formation neurons are poorly understood. As the transcription factor Gata2 is required for the development of all GABAergic neurons derived from the embryonic mouse midbrain, we hypothesized that the genes expressed downstream of Gata2 could contribute to the diversification of GABAergic neuron subtypes in this brain region. Here, we show that Gata2 is indeed required for the expression of several lineage-specific transcription factors in post-mitotic midbrain GABAergic neuron precursors. These include a homeodomain transcription factor Nkx2-2 and a SKI family transcriptional repressor Skor2, which are co-expressed in a restricted group of GABAergic precursors in the midbrain reticular formation. Both Gata2, and Nkx2-2 function is required for the expression of Skor2 in GABAergic precursors. In the adult mouse as well as rat midbrain, the Nkx2-2 and Skor2 expressing GABAergic neurons locate at the boundary of the ventrolateral periaqueductal gray and the midbrain reticular formation, an area shown to contain REM-off neurons regulating REM sleep. In addition to the characteristic localization, the Skor2 positive cells increase their activity upon REM sleep inhibition, send projections to a pontine region associated with sleep control and are responsive to orexins, consistent with the known properties of the midbrain REM-off neurons.

developmental biology↗

bioRxiv2021

KIR gene content imputation from single-nucleotide polymorphisms in the Finnish population

The killer cell immunoglobulin-like receptor (KIR) gene cluster on chromosome 19 encodes cell surface glycoproteins that bind class I human leukocyte antigen (HLA) molecules as well as some other ligands. Through regulation of natural killer (NK) cell activity KIRs participate in tumour surveillance and clearing viral infections. KIR gene gene copy number variation associates with the outcome of transplantations and susceptibility to immune-mediated diseases. Inferring KIR gene content from genetic variant data is therefore desirable for immunogenetic analysis, particularly in the context of growing biobank genome data collections that rely on genotyping by microarray. Here we describe a stand-alone and freely available gene content imputation for 12 KIR genes. The models were trained using 818 Finnish biobank samples genotyped for 5774 KIR-region SNPs and analysed for KIR gene content with targeted sequencing. Cross-validation results demonstrate a high mean overall accuracy of 99.2% (95% CI: 97.8-99.7%) which compares favourably with previous methods including short-read sequencing based approaches.

genomics↗

bioRxiv2019

An expanded analysis framework for multivariate GWAS connects inflammatory biomarkers to functional variants and disease

Multivariate methods are known to increase the statistical power of association detection, but they have lacked essential follow-up analysis tools necessary for understanding the biology underlying these associations. We developed a novel computational workflow for multivariate GWAS follow-up analyses, including fine-mapping and identification of the subset of traits driving associations (driver traits). Many follow-up tools require univariate regression coefficients which are lacking from multivariate results. Our method overcomes this problem by using Canonical Correlation Analysis to turn each multivariate association into its optimal univariate Linear Combination Phenotype (LCP). This enables an LCP-GWAS, which in turn generates the statistics required for follow-up analyses. We implemented our method on 12 highly correlated inflammatory biomarkers in a Finnish population-based study. Altogether, we identified 11 associations, four of which (F5, ABO, C1orf140 and PDGFRB) were not detected by biomarker-specific analyses. Fine-mapping identified 19 signals within the 11 loci and driver trait analysis determined the traits contributing to the associations. A phenome-wide association study on the 19 putative causal variants from the signals in 176,899 individuals from the FinnGen study revealed 53 disease associations (p < 1x10-4). Several reported pQTLs in the 11 loci provided orthogonal evidence for the biologically relevant functions of the putative causal variants. Our novel multivariate analysis workflow provides a powerful addition to standard univariate GWAS analyses by enabling multivariate GWAS follow-up and thus promoting the advancement of powerful multivariate methods in genomics.

genetics↗

bioRxiv2018

FinDonor 10 000 study: A cohort to identify iron depletion and factors affecting it in Finnish blood donors

Background and ObjectivesThere is increasing evidence that frequent blood donation depletes the iron stores of some blood donors. The FinDonor 10 000 study was set up to study iron status and factors affecting iron stores in Finnish blood donors. In Finland, iron supplementation for at-risk groups has been in place since the 1980s. Material and Methods2584 blood donors (N= 8003 samples) were recruited into the study alongside the standard donation at three donation sites in the capital region of Finland between 5/2015 and 12/2017. All participants were asked to fill out a questionnaire about their health and lifestyle. Blood samples were collected from the sample pouch of whole blood collection set, kept in cool temperature and processed centrally. Whole blood count, CRP, ferritin and sTFR were measured from the samples and DNA was isolated for GWAS studies. ResultsParticipant demographics, albeit in general similar to the general blood donor population in Finland, indicated some bias toward older and more frequent donors. Participation in the study increased median donation frequency of the donors. Analysis of the effect of time lag from the sampling to the analysis and the time of day when sample was drawn revealed small but significant time-dependent changes. ConclusionThe FinDonor cohort now provides us with tools to identify potential donor groups at increased risk of iron deficiency as factors explaining this risk. The increase in donation frequency during the study suggests that scientific projects can be used to increase the commitment of blood donors.

epidemiology↗

bioRxiv2018

HLA RNAseq reveals high allele-specific variability in mRNA expression

The HLA gene complex is the most important, single genetic factor in susceptibility to most diseases with autoimmune or autoinflammatory origin and in transplantation matching. The majority of the studies have focused on the huge allelic variation in these genes; only a few studies have explored differences in expression levels of HLA alleles. To study the expression levels of HLA alleles more systematically we utilised two different RNA sequencing methods. Illumina RNAseq has a high sequencing accuracy and depth but is limited by the short read length, whereas Oxford Nanopores technology can sequence long templates, but has a poor accuracy. We studied allelic mRNA levels of HLA class I and II alleles from peripheral blood samples of 50 healthy individuals. The results demonstrate large differences in mRNA expression levels between HLA alleles. The method can be applied to quantitate the expression differences of HLA alleles in various tissues and to evaluate the role of this type of variation in transplantation matching and susceptibility to autoimmune diseases.\n\nAuthor SummaryEven though HLA is widely studied less is known of its allele-specific expression. Due to the pivotal role of HLA in infection response, autoimmunity, and transplantation biology its expression surely must play a part as well. In hematopoietic stem cell transplantation the challenge often is to find a suitable HLA-matched donor due to the high allelic variation. Classical HLA typing methods do not take into account HLA allele-specific expression. However, differential allelic expression levels could be crucial in finding permissive mismatches in order to save a patients life. Additionally, differential HLA expression levels can lead into beneficial impact in viral clearance but also undesirable effects in autoimmune diseases. To study HLA expression we developed a novel RNAseq-based method to systematically characterize allele-specific expression levels of classical HLA genes. We tested our method in a set of 50 healthy individuals and found differential expression levels between HLA alleles as well as interindividual variability at the gene level. Since NGS is already well adopted in HLA research the next step could be to determine HLA allele-specific expression in addition to HLA allelic variation and HLA-disease association studies in various cells, tissues, and diseases.

immunology↗

bioRxiv2017

Accuracy of programs for the determination of HLA alleles from NGS data

The human leukocyte antigen (HLA) genes code for proteins that play a central role in the function of the immune system by presenting peptide antigens to T cells. As HLA genes show extremely high genetic polymorphism, HLA typing on the allele level is demanding and is based on DNA sequencing. Determination of HLA alleles is warranted as many HLA alleles are major genetic factors that confer susceptibility to autoimmune diseases and is important for the matching of HLA alleles in transplantation. Here, we compared the accuracy of several published HLA-typing algorithms that are based on next generation sequencing (NGS) data. As genome screens are becoming increasingly routine in research, we wanted to test how well HLA alleles can be deduced from genome screens not designed for HLA typing. The accuracies were assessed using datasets consisting of NGS data produced using the ImmunoSEQ platform, including the full 4 Mbp HLA segment, from 94 stem cell transplantation patients and exome sequences from the 1000 Genomes collection. When used with the default settings none of the methods gave perfect results for all the genes and samples. However, we found that ensemble prediction of the results or modifications of the settings could be used to improve accuracy. Most of the algorithms did not perform very well for the exome-only data. The results indicate that the use of these algorithms for accurate HLA allele determination based on NGS data is not straightforward.

immunology↗