Biology subjects

Jahn, K.

Publications and source records attributed to Jahn, K..

13 recordsLinked to original sources

bioRxiv2025

Cell lineage inference from mitochondrial mutations

Tracing genealogical relationships between cells and their molecular states is essential for understanding tissue development, maintenance and disease. We present mt-SCITE, a computational method that reconstructs cell division histories from mitochondrial mutations in single cells. On simulated and experimental benchmarks, mt-SCITE outperformed existing methods in terms of cell lineage reconstruction, and scaled to thousands of cells. Applied to expanded CD8+ T cells following vaccination, mt-SCITE resolved subclonal relationships and revealed clones with predominantly memory-like, effector-like, or mixed states. mt-SCITE robustly identifies clonally related populations and resolves substructure within them, providing a broadly applicable approach to study cell lineage development.

bioinformatics↗

bioRxiv2022

Joint copy number and mutation phylogeny reconstruction fromsingle-cell amplicon sequencing data

Reconstructing the history of somatic DNA alterations can help understand the evolution of a tumor and predict its resistance to treatment. Single-cell DNA sequencing (scDNAseq) can be used to investigate clonal heterogeneity and to inform phylogeny reconstruction. However, most existing phylogenetic methods for scDNAseq data are designed either for single nucleotide variants (SNVs) or for large copy number alterations (CNAs), or are not applicable to targeted sequencing. Here, we develop COMPASS, a computational method for inferring the joint phylogeny of SNVs and CNAs from targeted scDNAseq data. We evaluate COMPASS on simulated data and apply it to several datasets including a cohort of 123 patients with acute myeloid leukemia. COMPASS detected clonal CNAs that could be orthogonally validated with bulk data, in addition to subclonal ones that require single-cell resolution, some of which point toward convergent evolution.

bioinformatics↗

bioRxiv2021

Alpha-hemolysin of Staphylococcus aureus impairs thrombus formation

Toxins are key virulence determinants of pathogens and can impair the function of host immune cells including platelets. Insights into pathogen toxin interference with platelets will be pivotal to improve treatment of patients with bacterial bloodstream infections. In this study, we deciphered the effects of Staphylococcus aureus toxins -hemolysin, LukAB, LukDE and LukSF on human platelets and compared the effects with the pore forming toxin pneumolysin of Streptococcus pneumoniae. In contrast to pneumolysin, -hemolysin initially activates platelets as indicated by CD62P and IIb{beta}3 integrin expression, but the resulting pores also induce alterations in the phenotype of platelets and induce apoptosis of platelets. The presence of small amounts of -hemolysin (0.2 {micro}g/mL) in whole blood abrogates thrombus formation indicating that in systemic infections with S. aureus the stability of formed thrombi is impaired. This might be of high clinical relevance for S. aureus induced endocarditis of the aortic valves. Stabilizing the thrombi by inhibiting -hemolysin induced impairment of platelets likely reduces the risk for septic (micro-)embolization. However, in contrast to pneumolysin, -hemolysin induced platelets damage could not be neutralized by intravenous immune globulins. In contrast to -hemolysin, S. aureus bi-component pore forming leukocidins LukAB, LukED and LukSF do not bind to platelets and had no significant effect on platelet activation and viability. Main point 1: -hemolysin forms pores in platelets, which first activate but then result in apoptosis and impairs thrombus formation and stability Main point 2: Polyvalent immunoglobulins do not neutralize the mode of action of the toxin

microbiology↗

bioRxiv2021

Targeted biallelic integration of an inducible Caspase 9 suicide gene for safer iPSC-based cellular therapies

Drug-inducible suicide systems may help to minimize risks of cellular therapies due to the tumor forming potential of human induced pluripotent stem cells (hiPSCs). Recent research challenged the usefulness of such systems since rare drug-resistant subclones were observed that showed elimination or silencing of the transgene. We have introduced a drug-inducible Caspase9 suicide system (iCASP9) into the AAVS1 safe harbor locus of hiPSCs. In these cells, apoptosis could be efficiently induced in vitro. In mice, drug treatment generally led to rapid elimination of teratomas, but individual animals subsequently formed tumor tissue from monoallelic iCASP9 hiPSCs. Very rare drug-resistant subclones of monoallelic iCASP9 hiPSCs appeared in vitro with frequencies of ~ 3x10-8. Transgene elimination, presumably via Loss of Heterozygosity (LoH), or methylation of the CAG promoter but not methylation of the O_SCPLOWPPPC_SCPLOWO_SCPCAP1C_SCPCAPO_SCPLOWRC_SCPLOWO_SCPCAP12C_SCPCAPO_SCPLOWCC_SCPLOW locus were identified as underlying mechanisms. In contrast, we never observed any escapees from biallelic iCASP9 hiPSCs, even after treatment of up to 0.8 billion hiPSCs. In conclusion, biallelic integration of an iCASP9 system in the AAVS1 locus may substantially contribute to the safety level of iPSC-based therapies, which should be calculated by relating clonal escapee frequencies to the cell number in tumors of a size that is readily detectable during routine screening procedures.

molecular biology↗

bioRxiv2021

MinION nanopore sequencing provides similar methylation estimates to Sanger bisulfite sequencing in the TRPA1 promoter region

Bisulfite sequencing has long been considered the gold standard for measurement of DNA methylation at single CpG resolution. In the meantime, several new approaches have been developed, which are regarded as less error-prone. Since these errors were shown to be sequence-specific, we aimed to verify the methylation data of a particular region of the TRPA1 promoter obtained from our previous studies. For this purpose, we compared methylation rates obtained via direct bisulfite sequencing and nanopore sequencing. Thus, we were able to confirm our previous findings to a large extent.

molecular biology↗

bioRxiv2021

A statistical test for clonal exclusivity in tumour evolution

Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Pairs of mutations that are overrepresented in a clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical test, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our test for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 6 clonally exclusive and 2 clonally co-occurring gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways.

cancer biology↗

bioRxiv2020

Within-patient genetic diversity of SARS-CoV-2

SARS-CoV-2, the virus responsible for the current COVID-19 pandemic, is evolving into different genetic variants by accumulating mutations as it spreads globally. In addition to this diversity of consensus genomes across patients, RNA viruses can also display genetic diversity within individual hosts, and co-existing viral variants may affect disease progression and the success of medical interventions. To systematically examine the intra-patient genetic diversity of SARS-CoV-2, we processed a large cohort of 3939 publicly-available deeply sequenced genomes with specialised bioinformatics software, along with 749 recently sequenced samples from Switzerland. We found that the distribution of diversity across patients and across genomic loci is very unbalanced with a minority of hosts and positions accounting for much of the diversity. For example, the D614G variant in the Spike gene, which is present in the consensus sequences of 67.4% of patients, is also highly diverse within hosts, with 29.7% of the public cohort being affected by this coexistence and exhibiting different variants. We also investigated the impact of several technical and epidemiological parameters on genetic heterogeneity and found that age, which is known to be correlated with poor disease outcomes, is a significant predictor of viral genetic diversity. Author SummarySince it arose in late 2019, the new coronavirus (SARS-CoV-2) behind the COVID-19 pandemic has mutated and evolved during its global spread. Individual patients may host different versions, or variants, of the virus, hallmarked by different mutations. We examine the diversity of genetic variants coexisting within patients across a cohort of 3939 publicly accessible samples and 749 recently sequenced samples from Switzerland. We find that a small number of patients carry most of the diversity, and that patients with more diversity tend to be older. We also find that most of the diversity is concentrated in certain regions and positions of the virus genome. In particular, we find that a variant reported to increase infectivity is among the most diverse positions. Our study provides a large-scale survey of within-patient diversity of the SARS-CoV-2 genome.

genomics↗

bioRxiv2020

The Bourque Distances for Mutation Trees of Cancers

Mutation trees are rooted trees of arbitrary node degree in which each node is labeled with a mutation set. These trees, also referred to as clonal trees, are used in computational oncology to represent the mutational history of tumours. Classical tree metrics such as the popular Robinson-Foulds distance are of limited use for the comparison of mutation trees. One reason is that mutation trees inferred with different methods or for different patients usually contain different sets of mutation labels. Here, we generalize the Robinson-Foulds distance into a set of distance metrics called Bourque distances for comparing mutation trees. A connection between the Robinson-Foulds distance and the nearest neighbor interchange distance is also presented.

bioinformatics↗

bioRxiv2020

Single-cell copy number calling and event history reconstruction

Copy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations. We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to two breast cancer samples from different sequencing protocols.

cancer biology↗

bioRxiv2020

Clonal Evolution of Acute Myeloid Leukemia Revealed by High-Throughput Single-Cell Genomics

One of the pervasive features of cancer is the diversity of mutations found in malignant cells within the same tumor; a phenomenon called clonal diversity or intratumor heterogeneity. Clonal diversity allows tumors to adapt to the selective pressure of treatment and likely contributes to the development of treatment resistance and cancer recurrence. Thus, the ability to precisely delineate the clonal substructure of a tumor, including the evolutionary history of its development and the co-occurrence of its mutations, is necessary to understand and overcome treatment resistance. However, DNA sequencing of bulk tumor samples cannot accurately resolve complex clonal architectures. Here, we performed high-throughput single-cell DNA sequencing to quantitatively assess the clonal architecture of acute myeloid leukemia (AML). We sequenced a total of 556,951 cells from 77 patients with AML for 19 genes known to be recurrently mutated in AML. The data revealed clonal relationship among AML driver mutations and identified mutations that often co-occurred (e.g., NPM1/FLT3-ITD, DNMT3A/NPM1, SRSF2/IDH2, and WT1/FLT3-ITD) and those that were mutually exclusive (e.g., NRAS/KRAS, FLT3-D835/ITD, and IDH1/IDH2) at single-cell resolution. Reconstruction of the tumor phylogeny uncovered history of tumor development that is characterized by linear and branching clonal evolution patterns with latter involving functional convergence of separately evolved clones. Analysis of longitudinal samples revealed remodeling of clonal architecture in response to therapeutic pressure that is driven by clonal selection. Furthermore, in this AML cohort, higher clonal diversity ([≥]4 subclones) was associated with significantly worse overall survival. These data portray clonal relationship, architecture, and evolution of AML driver genes with unprecedented resolution, and illuminate the role of clonal diversity in therapeutic resistance, relapse and clinical outcome in AML.

cancer biology↗

bioRxiv2018

SCIΦ: Single-cell mutation identification via phylogenetic inference

Understanding the evolution of cancer is important for the development of appropriate cancer therapies. The task is challenging because tumors evolve as heterogeneous cell populations with an unknown number of genetically distinct subclones of varying frequencies. Conventional approaches based on bulk sequencing are limited in addressing this challenge as clones cannot be observed directly. Single-cell sequencing holds the promise of resolving the heterogeneity of tumors; however, it has its own challenges including elevated error rates, allelic dropout, and uneven coverage. Here, we develop a new approach to mutation detection in individual tumor cells by leveraging the evolutionary relationship among cells. Our method, called SCI{Phi}, jointly calls mutations in individual cells and estimates the tumor phylogeny among these cells. Employing a Markov Chain Monte Carlo scheme we robustly account for the various sources of noise in single-cell sequencing data. Our approach enables us to reliably call mutations in each single cell even in experiments with high dropout rates and missing data. We show that SCI{Phi} outperforms existing methods on simulated data and applied it to different real-world datasets, namely a whole exome breast cancer as well as a panel acute lymphoblastic leukemia dataset. Availability: https://github.com/cbg-ethz/SCIPhI

bioinformatics↗

bioRxiv2017

Integrative inference of subclonal tumour evolution from single-cell and bulk sequencing data

Understanding the evolutionary history and subclonal composition of a tumour represents one of the key challenges in overcoming treatment failure due to resistant cell populations. Most of the current data on tumour genetics stems from short read bulk sequencing data. While this type of data is characterised by low sequencing noise and cost, it consists of aggregate measurements across a large number of cells. It is therefore of limited use for the accurate detection of the distinct cellular populations present in a tumour and the unambiguous inference of their evolutionary relationships. Single-cell DNA sequencing instead provides data of the highest resolution for studying intra-tumour heterogeneity and evolution, but is characterised by higher sequencing costs and elevated noise rates. In this work, we develop the first computational approach that infers trees of tumour evolution from combined single-cell and bulk sequencing data. Using a comprehensive set of simulated data, we show that our approach systematically outperforms existing methods with respect to tree reconstruction accuracy and subclone identification. High fidelity reconstructions are obtained even with a modest number of single cells. We also show that combining single-cell and bulk sequencing data provides more realistic mutation histories for real tumours.

bioinformatics↗

bioRxiv2016

A statistical test on single-cell data reveals widespread recurrent mutations in tumor evolution

The infinite sites assumption, which states that every genomic position mutates at most once over the lifetime of a tumor, is central to current approaches for reconstructing mutation histories of tumors, but has never been tested explicitly. We developed a rigorous statistical framework to test the assumption with single-cell sequencing data. The framework accounts for the high noise and contamination present in such data. We found strong evidence for recurrent mutations at the same site in 8 out of 9 single-cell sequencing datasets from human tumors. Six cases involved the loss of earlier mutations, five of which occurred at sites unaffected by large scale genomic deletions. Two cases exhibited parallel mutation, including the dataset with the strongest evidence of recurrence. Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity.

cancer biology↗