Biology subjects

Nordlund, J.

Publications and source records attributed to Nordlund, J..

At least 19 recordsLinked to original sources

bioRxiv2026

upSPLAT: Early-Barcoded Library Preparation for Cost-Effective Population-Scale Genomics

Advances in high-throughput sequencing have substantially reduced sequencing costs, yet library preparation remains a major financial and logistical bottleneck, particularly for high-throughput applications or low-quality DNA inputs. Here, we introduce upscaled Splinted Ligation Adapter Tagging (upSPLAT), a library preparation strategy that combines early sample barcoding with single-stranded splinted ligation to enable highly multiplexed pooled sequencing at substancially reduced cost. upSPLAT supports flexible high-plex pooling and reduces per-sample library preparation costs by approximately 10-fold compared to conventional workflows. By leveraging single-strand ligation, upSPLAT is compatible with a wide range of DNA inputs, including degraded, damaged or denatured double stranded DNA, bisulfite or enzymatically converted DNA, and viral single-stranded DNA. We present two complementary workflows and evaluate their performance across multiple species and DNA qualities, demonstrating robust demultiplexing, uniform sample representation, and low barcode cross-assignment. Together, upSPLAT provides a scalable, cost-effective solution for sequencing-based studies requiring large sample numbers while preserving individual-level information.

genomics↗

bioRxiv2025

Comparative analysis of whole genome amplification kits for single cell genome analysis

Whole genome amplification (WGA) enables detailed analysis of genomic heterogeneity at the cellular level. Here, we systematically compare two commercially available WGA technologies on single cells; Multiple Displacement Amplification in the Qiagen REPLI-g kit (MDA/REPLI-g) and Primary Template-directed Amplification in the BioSkryb ResolveDNA kit (PTA/ResolveDNA). Beyond quality control and genomic profiling, we assessed a cost-effective array-based approach as a complement to low-pass sequencing. Consistent with available benchmarks, PTA/ResolveDNA sequencing libraries consistently outperformed MDA/REPLI-g across quality metrics, including genome coverage breadth and uniformity. CNV calls from PTA-cells showed higher accuracy and sensitivity, including reliable detection of small CNVs at low sequencing depth. SNP array-based analysis corroborated sequencing-based findings, and additionally provided allelic information. Notably, CNV detection from SNP arrays matched the performance of low-pass sequencing, supporting its utility as an cost-effective approach for WGA quality control and single-cell CNV profiling.

genomics↗

bioRxiv2025

Whole genome sequencing with AVITI and NovaSeq X Plus reveals comparable performance with contextual biases

Element Biosciences avidity sequencing has emerged as a competing technology to Illuminas short read sequencing platform. Prior benchmarks of avidity sequencing have not included the latest Illumina NovaSeq X/X Plus instruments with XLEAP chemistry. Here, we have run PCR-free whole genome sequencing on four human tumor cell lines using both Illumina NovaSeq X Plus and Element AVITI instruments. AVITI showed low duplication rates and reported higher base qualities, the latter contributed to improved mapping confidence and fewer spurious variant candidates. Both platforms were found to be highly comparable when benchmarking variant calling, with AVITI only providing a minor improvement on INDELs at lower coverages. Stratifying by genomic context revealed further differences, where AVITI genome coverage and variant calls were superior in high GC regions while being inferior in GC homopolymers. Error rate analysis highlighted further differences between the platforms, in particular AVITI in some instances displayed an increased error rate on read 2 related to short fragments. AVITI error rate was also found to be more stable downstream of repetitive regions, except for GC homopolymers. We further found that AVITI sequencing was sensitive to G-quadruplex motifs. Overall, despite these identified differences, both platforms performed highly comparable for variant analysis.

genomics↗

bioRxiv2025

Interferon-α-Driven Stratification of B Cell Reveals Metabolic Reprogramming of Double Negative, Naive and Transitional cell subsets and Refines Molecular Classification in Söjgren's Disease.

Sjogrens disease (SjD) is a chronic autoimmune condition marked by lymphocytic infiltration of exocrine glands and production of autoantibodies such as anti-SSA/Ro, anti-SSB/La, and rheumatoid factor. B lymphocytes play a central role in disease pathogenesis, driving autoantibody production and glandular damage, and contributing to lymphomagenesis. Despite promising therapies, no effective treatment is currently available, partly due to the biological and clinical heterogeneity of the disease. While interferon (IFN) signatures and B cell-related markers are used for patient stratification, their integration remains unexplored. This study analyzed B cell transcriptional and metabolic profiles using bulk transcriptomic, clinical, and flow cytometry data from the PRECISESADS consortium, alongside public single-cell RNA-sequencing datasets. A B cell-specific IFN- signature was established to stratify patients into IFN-positive and IFN-negative groups. Both showed reduced oxidative phosphorylation (OXPHOS) and translation in B cell subsets, suggesting a shared pre-metabolic state. IFN-positive patients, however, displayed additional features, including enhanced glycolysis, amino acid and lipid metabolism, autophagy, and NF-{kappa}B signaling. They also showed an expansion of IFN-activated naive (Naive IFN), Transitional, and double-negative (DN) B cells, particularly DN2 and DN2_CXCR3 subsets, which have been linked in the literature to autoreactivity and lymphoma development. The IFN signature in naive B cells and DN2 correlated with elevated anti-SSA/Ro and anti-SSB/La titers, while only naive B cells showed an association with increased histological focus scores. These findings support the relevance of a B cell-specific IFN signature in stratifying SjD patients and suggest new metabolic and transcriptional targets for disease monitoring and therapeutic development. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/672530v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@fd8a4eorg.highwire.dtl.DTLVardef@183241dorg.highwire.dtl.DTLVardef@b7f2d9org.highwire.dtl.DTLVardef@a89349_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2024

Error reduction in leukemia machine learning classification with conformal prediction

PurposeRecent advances in machine learning (ML) have led to the development of classifiers that predict molecular subtypes of acute lymphoblastic leukemia (ALL) using RNA sequencing (RNA-seq) data. While these models have shown promising results, they often lack robust performance guarantees. The aim of this study was three-fold: to quantify the uncertainty of these classifiers; to provide prediction sets that control the false negative rate (FNR); and to perform implicit reduction by transforming incorrect predictions into uncertain predictions. MethodsConformal prediction is a distribution-agnostic framework for generating statistically calibrated prediction sets whose size reflects model uncertainty. In this study, we applied an extension called conformal risk control to ALLIUM, an RNA-seq ALL subtype classifier. Leveraging RNA-seq data from 1042 patient samples taken at diagnosis, we developed a multi-class conformal predictor, ALLCoP, which generates statistically guaranteed FNR-controlled prediction sets. ResultsALLCoP was able to create prediction sets with specified FNR tolerances ranging from 7.5-30%. In a validation cohort, ALLCoP successfully reduced the FNR of the ALLIUM classifier from 8.95% to 3.5%. For cases whose subtype was not previously known, the use of ALLCoP was able to reduce the occurrence of empty predictions from 37% to 17%. Notably, up to 34% of the multiple-class prediction sets included the PAX5alt subtype, suggesting that increased prediction set size may reflect secondary aberrations and biological complexity, contributing to classifier uncertainty. Finally, ALLCoP was validated on two additional RNA-seq ALL subtype classifiers, ALLSorts and ALLCatchR. ConclusionOur results highlight the potential of conformal prediction in enhancing the use of oncological RNA-seq subtyping classifiers and also in uncovering additional molecular aberrations of potential clinical importance.

bioinformatics↗

bioRxiv2024

Comprehensive multi-omics profiling of a healthy human cohort

Multi-omics approaches can offer powerful insights into personalized biomarker profiles relevant for disease diagnosis, prognosis, and therapeutics. However, separating meaningful biological variability from technical noise remains a major challenge. The EATRIS-Plus consortium analyzed blood samples from 127 healthy adults across six omics layers using twelve platforms, resulting in one of the most comprehensive multi-omics profiling datasets of healthy individuals available to date. We applied reproducible workflows to analyze and integrate these data, revealing several key findings. Sex significantly influenced all omics layers, emphasizing the importance of sex-balanced study designs. Age could be accurately predicted using epigenetic clocks, achieving high performance with our high-resolution enzymatic methylation sequencing data (R2 = 0.90), whereas candidate aging biomarkers were identified across all omics layers. The resulting dataset provides reference ranges in healthy individuals for abundance and variability of omics features, enabling robust power analyses, sample size estimations, and benchmarking of multi-omics integration methods. This resource can guide future biomarker discovery and personalized health research and was made FAIR-compliant and publicly available via the ClinData Portal (https://clindata.imtm.cz) and a Zenodo repository (https://doi.org/10.5281/zenodo.17514796).

bioinformatics↗

bioRxiv2024

Exploring a pico-well based scRNA-seq method (HIVE) for simplified processing of equine bronchoalveolar lavage cells

Single-cell RNA sequencing (scRNA-seq) is a valuable tool for investigating cellular heterogeneity in diseases such as equine asthma (EA). This study evaluates the HIVE scRNA-seq method, a pico-well-based technology, for processing bronchoalveolar lavage (BAL) cells from horses with EA. The HIVE method offers practical advantages, including compatibility with both field and clinical settings, as well as a gentle workflow suited for handling sensitive cells. Our results show that the major cell types in equine BAL were successfully identified; however, the proportions of T cells and macrophages deviated from cytological expectations, with macrophages being overrepresented and T cells underrepresented. Despite these limitations, the HIVE method confirmed previously identified T cell and macrophage subpopulations and defined other BAL cell subsets. However, compared to previous studies T helper subsets were less clearly defined. Additionally, consistent with previous scRNA-seq studies, the HIVE method detected fewer granulocytes and mast cells than anticipated in the total BAL samples. Nevertheless, applying the method to purified mast cells recovered an expected number of cells. A small set of eosinophils were also detected which have not been characterized in earlier studies. In summary these findings suggest that while the HIVE method shows promise for certain applications, further optimization is needed to improve the accuracy of cell type representation, particularly for granulocytes and mast cells, in BAL samples.

genomics↗

bioRxiv2024

Mapping the Spatial Proteome of Leukemia Cells Undergoing Fludarabine Treatment

Recent advancements in spatial biology have revolutionized our understanding of the organization and functional dynamics of cells and tissues. In this study, we applied Molecular Pixelation (MPX), a single-cell spatial proteomics assay, to investigate the modulation of the cell surface proteome in an in vitro drug screening model using the ETV6::RUNX1 acute lymphoblastic leukemia (ALL) cell line, Reh. Specifically, we focused on the in vitro response to fludarabine, a chemotherapeutic agent used prior to allogenic stem cell transplantation and chimeric antigen receptor (CAR)-T cell therapy in high-risk, refractory, or relapsed ALL patients. Using MPX, we quantified changes in protein abundance, spatial distribution, and colocalization of 76 targeted cell surface proteins in Reh cells before and after fludarabine treatment. Our analysis revealed 25 proteins with altered abundance, 24 proteins with increased polarity, and 138 protein pairs with modified colocalization following treatment. Notably, the tetraspanins CD82 and CD53, which are known for their roles in chemotherapy resistance, exhibited increased abundance, polarization, and colocalization post-treatment, suggesting their potential as a therapeutic scaffold. These findings underscore the unique ability of spatially resolved single-cell proteomics to uncover nuanced cellular responses that would otherwise remain undetected.

cancer biology↗

bioRxiv2023

COMPARISON OF HIGH-THROUGHPUT SINGLE-CELL RNA-SEQ METHODS FOR EX VIVO DRUG SCREENING

Functional precision medicine (FPM) aims to optimize patient-specific drug selection based on the unique characteristics of their cancer cells. Recent advancements in high throughput ex vivo drug profiling have accelerated interest in FPM. Here, we present a proof-of-concept study for an integrated experimental system that incorporates ex vivo treatment response with a single-cell gene expression output enabling barcoding of several drug conditions in one single-cell sequencing experiment. We demonstrate this through a proof-of-concept investigation focusing on the glucocorticoid-resistant acute lymphoblastic leukemia (ALL) E/R+ Reh cell line. Three different single-cell transcriptome sequencing (scRNA-seq) approaches were evaluated, each exhibiting high cell recovery and accurate tagging of distinct drug conditions. Notably, our comprehensive analysis revealed variations in library complexity, sensitivity (gene detection), and differential gene expression detection across the methods. Despite these differences, we identified a substantial transcriptional response to fludarabine, a highly relevant drug for treating high-risk ALL, which was consistently recapitulated by all three methods. These findings highlight the potential of our integrated approach for studying drug responses at the single-cell level and emphasize the importance of method selection in scRNA-seq studies. Finally, our data encompassing 27,327 cells are freely available to extend to future scRNA-seq methodological comparisons.

genomics↗

bioRxiv2023

A complete digital karyotype of the B-cell leukemia REH cell line resolved by long-read sequencing

The B-cell acute lymphoblastic leukemia (ALL) cell line REH, with the t(12;21) ETV6-RUNX1 translocation, is known to have a complex karyotype defined by a series of large-scale chromosomal rearrangements. Taken from a 15-year-old at relapse, the cell line offers a practical model for the study of high-risk pediatric B-ALL patients. In recent years, short-read DNA and RNA sequencing have emerged as a complement to analog karyotyping techniques in the resolution of structural variants in an oncological context. However, it is challenging to create a comprehensive digital karyotype of a genome with these techniques alone. Here, we explore the integration of long-read PacBio and Oxford Nanopore whole genome sequencing (WGS), IsoSeq RNA-sequencing, and short-read sequencing to create a detailed digital karyotype of the REH cell line. WGS refined the breakpoints of known aberrations and clarified the molecular traits of disrupted ALL-associated genes BTG1 and TBL1XR1, as well as the glucocorticoid receptor NR3C1. Several previously underreported structural variants were also uncovered, including deletions affecting the ALL-associated genes VPREB1 and NFATC1. Meanwhile, transcriptome sequencing identified seven fusion genes within the genomic breakpoints. Together, our extensive whole-genome investigation makes high-quality open-source data available to the leukemia genomics community. KEY POINTSO_LIA complete digital karyotype of the REH cell line was produced with short- and long-read DNA and RNA sequencing technologies. C_LIO_LIThe study enabled precise identification of structural variants, and the fusion genes expressed as the result of these variants. C_LI

cancer biology↗

bioRxiv2022

Single cell transcriptomics delineates the immune-cell landscape in equine lower airways and reveals upregulation of the FKBP5 gene in horses with asthma

BackgroundEquine asthma (EA) is a heterogenous, complex disease with a significant negative impact on horse welfare and performance. EA and human asthma have fundamental similarities, making EA a useful large animal disease model. Bronchoalveolar lavage (BAL) fluid provides a snapshot sample of the immune cells occupying the alveolar space and is one of the most relevant sample types for studies of chronic inflammation in the lung. In this study, we sequenced single equine BAL-cells in order to study the immune cell landscape of the respiratory tract of horses diagnosed with mild-to-moderate EA and healthy controls. ResultsScRNA-seq analysis of ~63,000 cells from eleven horses diagnosed with mild-moderate asthma (mEA) and eight healthy controls were performed using the Drop-Seq technology. We identified five major immune cell populations in equine BAL; alveolar macrophages (AM), T cells, neutrophils, mast cells and dendritic cells, as well as subtypes thereof. The cellular subtypes demonstrated herein have previously not been characterized in horses. Differential gene expression analysis revealed upregulation of genes in mEA horses, including FKBP5 and CCL24, which have previously been associated with asthma in other species. The most significantly upregulated gene across the cell types in EA was FKBP5, a chaperone protein involved in regulating the assembly, activity, and sensitivity of the glucocorticoid receptor ConclusionHerein we demonstrate the first comprehensive scRNA-seq map of the immune-cell populations in BAL from horses with asthma and healthy individuals. The glucocorticoid receptor associated protein FKBP5 was identified as a potential biomarker for EA.

genomics↗

bioRxiv2022

Quartet DNA reference materials and datasets for comprehensively evaluating germline variants calling performance

Current methods for evaluating the accuracy of germline variant calls are restricted to easy-to-detect high-confidence regions, thus ignoring a substantial portion of difficult variants beyond the benchmark regions. We established four DNA reference materials from immortalized cell lines derived from a Chinese Quartet including parents and monozygotic twins. We integrated benchmark calls of 4.2 million small variants and 15,000 structural variants from multiple platforms and bioinformatic pipelines for evaluating the reliability of germline variant calls inside the benchmark regions. The genetic built-in-truth of the Quartet family design not only improved sensitivity of benchmark calls by removing additional false positive variants with apparently high quality, but also enabled estimation of the precision of variants calls outside the benchmark regions. Batch effects of variant calling in large-scale DNA sequencing efforts can be effectively identified with the concurrent use of the Quartet DNA reference materials along with study samples, and can be alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Matched RNA and protein reference materials were also established in the Quartet project, thereby enabling benchmark calls constructed from DNA reference materials for evaluation of variants calling performance on RNA and protein data. The Quartet DNA reference materials from this study are a resource for objective and comprehensive assessment of the accuracy of germline variant calls throughout the whole-genome regions.

bioinformatics↗

bioRxiv2021

scSPLAT, a scalable plate-based protocol for single cell WGBS library preparation

DNA methylation is a central epigenetic mark that has diverse roles in gene regulation, development, and maintenance of genome integrity. 5 methyl cytosine (5mC) can be interrogated at base resolution in single cells by using bisulfite sequencing (scWGBS). Several different scWGBS strategies have been described in recent years to study DNA methylation in single cells. However, there remain limitations with respect to cost-efficiency and yield. Herein, we present a new development in the field of scWGBS library preparation; single cell Splinted Ligation Adapter Tagging (scSPLAT). scSPLAT employs a pooling strategy to facilitate sample preparation at a higher scale and throughput than previously possible. We demonstrate the accuracy and robustness of the method by generating data from 225 single K562 cells and from 309 single liver nuclei and compare scSPLAT against other scWGBS methods. MotivationscWGBS library preparation in a one-cell-per-library format presents practical and economical constraints to the number of cells that can be analyzed in a research project. In addition, most of the current scWGBS methods suffer from low read alignment rates. We present a scWGBS protocol which mitigates these issues, empowering single-cell DNA methylation analysis at an increased scale.

genomics↗

bioRxiv2021

Whole Genome and Exome Sequencing Reference Datasets from A Multi-center and Cross-platform Benchmark Study

With the rapid advancement of sequencing technologies in the past decade, next generation sequencing (NGS) analysis has been widely applied in cancer genomics research. More recently, NGS has been adopted in clinical oncology to advance personalized medicine. Clinical applications of precision oncology require accurate tests that can distinguish tumor-specific mutations from errors or artifacts introduced during NGS processes or data analysis. Therefore, there is an urgent need to develop best practices in cancer mutation detection using NGS and the need for standard reference data sets for systematically benchmarking sequencing platforms, library protocols, bioinformatics pipelines and for measuring accuracy and reproducibility across platforms and methods. Within the SEQC2 consortium context, we established paired tumor-normal reference samples, a human triple-negative breast cancer cell line and a matched normal cell line derived from B lymphocytes. We generated whole-genome (WGS) and whole-exome sequencing (WES) data using 16 NGS library preparation protocols, seven sequencing platforms at six different centers. We systematically interrogated somatic mutations in the paired reference samples to identify factors affecting detection reproducibility and accuracy in cancer genomes. These large cross-platform/site WGS and WES datasets using well-characterized reference samples will represent a powerful resource for benchmarking NGS technologies, bioinformatics pipelines, and for the cancer genomics studies.

bioinformatics↗

bioRxiv2020

The SEQC2 Epigenomics Quality Control (EpiQC) Study: Comprehensive Characterization of Epigenetic Methods, Reproducibility, and Quantification

Cytosine modifications in DNA such as 5-methylcytosine (5mC) underlie a broad range of developmental processes, maintain cellular lineage specification, and can define or stratify cancer and other diseases. However, the wide variety of approaches available to interrogate these modifications has created a need for harmonized materials, methods, and rigorous benchmarking to improve genome-wide methylome sequencing applications in clinical and basic research. Here, we present a multi-platform assessment and a global resource for epigenetics research from the FDAs Epigenomics Quality Control (EpiQC) Group. The study design leverages seven human cell lines that are designated as reference materials and publicly available from the National Institute of Standards and Technology (NIST) and Genome in a Bottle (GIAB) consortium. These samples were subject to a variety of genome-wide methylation interrogation approaches across six independent laboratories, with a primary focus was on 5-methylcytosine modifications. Each sample was processed in two or more technical replicates by three whole-genome bisulfite sequencing (WGBS) protocols (TruSeq DNA methylation, Accel-NGS MethylSeq, and SPLAT), oxidative bisulfite sequencing (TrueMethyl), one enzymatic deamination method (EMseq), targeted methylation sequencing (Illumina Methyl Capture EPIC), and single-molecule long-read nanopore sequencing from Oxford Nanopore Technologies. After rigorous quality assessment and comparison to Illumina EPIC methylation microarrays and testing on a range of algorithms (Bismark, BitmapperBS, BWAMeth, and GemBS), we found overall high concordance between assays (R=0.87-R0.93), differences in efficency of read mapping and CpG capture and coverage, and platform performance. The data provided herein can guide continued used of these reference materials in epigenomics assays, as well as provide best practices for epigenomics research and experimental design in future studies.

genomics↗

bioRxiv2020

The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome

We report a systematic analysis of the biological and clinical implications of DNA methylation variability in five categories of B-cell tumors derived from B cells spanning the entire maturation spectrum. We used 2056 primary samples including training and validation series and show that 88% of the human DNA methylome is dynamically modulated under normal and neoplastic conditions. B-cell tumors display both epigenetic imprints of their cellular origin and de novo, disease-specific epigenetic alterations that in part are related to differential transcription factor binding. These differential methylation patterns were used by a machine-learning approach to create a diagnostic algorithm that accurately classifies 14 B-cell tumor entities and subtypes with different clinical management. Beyond this, we identified extensive patient-specific epigenetic variability targeting constitutively silenced chromatin regions, a phenomenon we could relate to the proliferative history of normal and neoplastic B cells. We observed that, depending on the maturation stage of the tumor cell of origin, mitotic activity leaves different imprints into the DNA methylome. Subsequently, we constructed a novel DNA methylation-based mitotic clock called epiCMIT (epigenetically-determined Cumulative MIToses), whose lapse magnitude represents a strong independent prognostic variable within specific B-cell tumor subtypes and is associated with particular driver genetic alterations. Our findings reveal DNA methylation as a holistic tracker of B-cell tumor developmental history, with implications in the differential diagnosis and prediction of the outcome of the patients.

cancer biology↗

bioRxiv2019

Loss of Y in leukocytes, dysregulation of autosomal immune genes and disease risks

Mosaic loss of chromosome Y (LOY) in leukocytes has been associated with many diseases, yet it remains unclear whether this form of clonal mosaicism exerts a direct physiological effect. Here we perform single-cell and bulk RNA sequencing in leukocytes, observing considerable variation in the rate of LOY across individuals, cell types and disease state. Cells with LOY demonstrated a profound degree of transcriptional dysregulation impacting [~]500 autosomal genes. These genes are preferentially involved in immune functions but also encode proteins with roles in other diverse biological processes. Our findings highlight a surprisingly broad role for chromosome Y challenging the view of it as a "genetic wasteland". Furthermore, they support the hypothesis that altered immune function in leukocytes is a mechanism directly linking LOY to disease.

immunology↗

bioRxiv2019

Establishing reference samples for detection of somatic mutations and germline variants with NGS technologies

We characterized two reference samples for NGS technologies: a human triple-negative breast cancer cell line and a matched normal cell line. Leveraging several whole-genome sequencing (WGS) platforms, multiple sequencing replicates, and orthogonal mutation detection bioinformatics pipelines, we minimized the potential biases from sequencing technologies, assays, and informatics. Thus, our \"truth sets\" were defined using evidence from 21 repeats of WGS runs with coverages ranging from 50X to 100X (a total of 140 billion reads). These \"truth sets\" present many relevant variants/mutations including 193 COSMIC mutations and 9,016 germline variants from the ClinVar database, nonsense mutations in BRCA1/2 and missense mutations in TP53 and FGFR1. Independent validation in three orthogonal experiments demonstrated a successful stress test of the truth set. We expect these reference materials and \"truth sets\" to facilitate assay development, qualification, validation, and proficiency testing. In addition, our methods can be extended to establish new fully characterized reference samples for the community.

bioinformatics↗