Abstract
Motivation
Droplet-based single-cell RNA sequencing (scRNA-seq) is widely used in biomedical research for interrogating the transcriptomes of single cells on a large scale. Pooling and ...processing cells from different samples together can reduce costs and batch effects. To pool cells, they are often first labeled with hashtag oligonucleotides (HTOs). These HTOs are sequenced alongside the cells’ RNA in the droplets and subsequently used to computationally assign each droplet to its sample of origin, a process referred to as demultiplexing. Accurate demultiplexing is crucial but can be challenging due to background HTOs, low-quality cells/cell debris, and multiplets.
Results
A new demultiplexing method based on negative binomial regression mixture models is introduced. The method, called demuxmix, implements two significant improvements. First, demuxmix’s probabilistic classification framework provides error probabilities for droplet assignments that can be used to discard uncertain droplets and inform about the quality of the HTO data and the success of the demultiplexing process. Second, demuxmix utilizes the positive association between detected genes in the RNA library and HTO counts to explain parts of the variance in the HTO data resulting in improved droplet assignments. The improved performance of demuxmix compared with existing demultiplexing methods is assessed using real and simulated data. Finally, the feasibility of accurately demultiplexing experimental designs where non-labeled cells are pooled with labeled cells is demonstrated.
Availability and implementation
R/Bioconductor package demuxmix (https://doi.org/doi:10.18129/B9.bioc.demuxmix)
Bisulfite sequencing is currently the gold standard to obtain genome-wide DNA methylation profiles in eukaryotes. In contrast to the rapid development of appropriate pre-processing and alignment ...software, methods for analyzing the resulting methylation profiles are relatively limited so far. For instance, an appropriate pipeline to detect DNA methylation differences between cancer and control samples is still required.
We propose an algorithm that detects significantly differentially methylated regions in data obtained by targeted bisulfite sequencing approaches, such as reduced representation bisulfite sequencing. In a first step, this approach tests all target regions for methylation differences by taking spatial dependence into account. A false discovery rate procedure controls the expected proportion of incorrectly rejected regions. In a second step, the significant target regions are trimmed to the actually differentially methylated regions. This hierarchical procedure detects differentially methylated regions with increased power compared with existing methods.
R/Bioconductor package BiSeq.
Supplementary Data are available at Bioinformatics online.
We report a multi-omic resource generated by applying quantitative trait locus (xQTL) analyses to RNA sequence, DNA methylation and histone acetylation data from the dorsolateral prefrontal cortex of ...411 older adults who have all three data types. We identify SNPs significantly associated with gene expression, DNA methylation and histone modification levels. Many of these SNPs influence multiple molecular features, and we demonstrate that SNP effects on RNA expression are fully mediated by epigenetic features in 9% of these loci. Further, we illustrate the utility of our new resource, xQTL Serve, by using it to prioritize the cell type(s) most affected by an xQTL. We also reanalyze published genome wide association studies using an xQTL-weighted analysis approach and identify 18 new schizophrenia and 2 new bipolar susceptibility variants, which is more than double the number of loci that can be discovered with a larger blood-based expression eQTL resource.
The past decade has seen the maturation of multiple different forms of high‐dimensional molecular profiling to the point that these methods could be deployed in initially hundreds and more recently ...thousands of human samples. In the field of Alzheimer's disease (AD), these profiles have been applied to the target organ: the aging brain. In a growing number of cases, the same samples were profiled with multiple different approaches, yielding genetic, transcriptomic, epigenomic and proteomic data. Here, we review lessons learned so far as we move beyond quantitative trait locus (QTL) analyses which map the effect of genetic variation on molecular features to integrate multiple levels of “omic” data in an effort to identify the molecular drivers of AD. One thing is clear: no single layer of molecular or “omic” data is sufficient to capture the variance of AD or aging‐related cognitive decline. Nonetheless, reproducible findings are emerging from current efforts, and there is evidence of convergence using different approaches. Thus, we are on the cusp of an acceleration of truly integrative studies as the availability of large numbers of well‐characterized brain samples profiled in three or more dimensions enables the testing, comparison and refinement of analytic methods with which to dissect the molecular architecture of the aging brain.
Epigenomic changes are an important feature of malignant tumors. How tumor aggressiveness is affected by DNA methylation of specific loci is largely unexplored. In genome‐wide DNA methylation ...analyses, we identified the KCa3.1 channel gene (KCNN4) promoter to be hypomethylated in an aggressive non–small‐cell lung carcinoma (NSCLC) cell line and in patient samples. Accordingly, KCa3.1 expression was increased in more aggressive NSCLC cells. Both findings were strong predictors for poor prognosis in lung adenocarcinoma. Increased KCa3.1 expression was associated with aggressive features of NSCLC cells. Proliferation and migration of pro‐metastatic NSCLC cells depended on KCa3.1 activity. Mechanistically, elevated KCa3.1 expression hyperpolarized the membrane potential, thereby augmenting the driving force for Ca2+ influx. KCa3.1 blockade strongly reduced the growth of xenografted NSCLC cells in mice as measured by positron emission tomography–computed tomography. Thus, loss of DNA methylation of the KCNN4 promoter and increased KCa3.1 channel expression and function are mechanistically linked to poor survival of NSCLC patients.
What's New?
Another possible avenue for thwarting metastasis has been found: the potassium channel KCa3.1. Aggressiveness in lung cancer (NSCLC) is linked to methylation of the potassium channel promoter gene. When the promoter is under‐methylated, more of the potassium channel protein is produced, and the cancer cells proliferate and spread more aggressively. The authors also showed that blocking KCa3.1 stops the cells from dividing and spreading, both in vitro and in mice, suggesting that KCa3.1 could make a promising therapeutic target.
Acute promyelocytic leukemia (APL), a cytogenetically distinct subtype of acute myeloid leukemia (AML), characterized by the t(15;17)-associated PML-RARA fusion, has been successfully treated with ...therapy utilizing all-trans-retinoic acid (ATRA) to differentiate leukemic blasts. However, among patients with non-APL AML, ATRA-based treatment has not been effective. Here we show that, through epigenetic reprogramming, inhibitors of lysine-specific demethylase 1 (LSD1, also called KDM1A), including tranylcypromine (TCP), unlocked the ATRA-driven therapeutic response in non-APL AML. LSD1 inhibition did not lead to a large-scale increase in histone 3 Lys4 dimethylation (H3K4(me2)) across the genome, but it did increase H3K4(me2) and expression of myeloid-differentiation-associated genes. Notably, treatment with ATRA plus TCP markedly diminished the engraftment of primary human AML cells in vivo in nonobese diabetic (NOD)-severe combined immunodeficient (SCID) mice, suggesting that ATRA in combination with TCP may target leukemia-initiating cells. Furthermore, initiation of ATRA plus TCP treatment 15 d after engraftment of human AML cells in NOD-SCID γ (with interleukin-2 (IL-2) receptor γ chain deficiency) mice also revealed the ATRA plus TCP drug combination to have a potent anti-leukemic effect that was superior to treatment with either drug alone. These data identify LSD1 as a therapeutic target and strongly suggest that it may contribute to AML pathogenesis by inhibiting the normal pro-differentiative function of ATRA, paving the way for new combinatorial therapies for AML.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Mitochondrial dysfunction is a feature of neurodegenerative diseases, including Alzheimer's disease (AD). Changes in the mitochondrial DNA copy number (mtDNAcn) and increased mitochondrial DNA ...mutation burden have both been associated with neurodegenerative diseases and cognitive decline. This study aims to systematically identify which common brain pathologies in the aged human brain are associated with mitochondrial recalibrations and to disentangle the relationship between these pathologies, mtDNAcn, mtDNA heteroplasmy, aging, neuronal loss, and cognitive function.
Whole-genome sequencing data from n = 1361 human brain samples from 5 different regions were used to quantify mtDNAcn as well as heteroplasmic mtDNA point mutations and small indels. Brain samples were assessed for 10 common pathologies. Annual cognitive test results were used to assess cognitive function proximal to death. For a subset of samples, neuronal proportions were estimated from RNA-seq profiles, and mass spectrometry was used to quantify the mitochondrial protein content of the tissue.
mtDNAcn was 7-14% lower in AD relative to control participants. When accounting for all 10 common neuropathologies, only tau was significantly associated with lower mtDNAcn in the dorsolateral prefrontal cortex. In the posterior cingulate cortex, TDP-43 pathology demonstrated a distinct association with mtDNAcn. No changes were observed in the cerebellum, which is affected late by pathologies. Neither age nor gender was associated with mtDNAcn in the studied brain regions when adjusting for pathologies. Mitochondrial content and mtDNAcn independently explained variance in cognitive function unaccounted by pathologies, implicating complex mitochondrial recalibrations in cognitive decline. In contrast, mtDNA heteroplasmy levels increased by 1.5% per year of life in the cortical regions, but displayed no association with any of the pathologies or cognitive function.
We studied mtDNA quantity and quality in relation to mixed pathologies of aging and showed that tau and not amyloid-β is primarily associated with reduced mtDNAcn. In the posterior cingulate cortex, the association of TDP-43 with low mtDNAcn points to a vulnerability of this region in limbic-predominant age-related TDP-43 encephalopathy. While we found low mtDNAcn in brain regions affected by pathologies, the absence of associations with mtDNA heteroplasmy burden indicates that mtDNA point mutations and small indels are unlikely to be involved in the pathogenesis of late-onset neurodegenerative diseases.
Epigenetic clocks are calculated by combining DNA methylation states across select CpG sites to estimate biologic age, and have been noted as the most successful markers of biologic aging to date. ...Yet, limited research has considered epigenetic clocks calculated in brain tissue. We used DNA methylation states in dorsolateral prefrontal cortex specimens from 721 older participants of the Religious Orders Study and Rush Memory and Aging Project, to calculate DNA methylation age using four established epigenetic clocks: Hannum, Horvath, PhenoAge, GrimAge, and a new Cortical clock. The four established clocks were trained in blood samples (Hannum, PhenoAge, GrimAge) or using 51 human tissue and cell types (Horvath); the recent Cortical clock is the first trained in postmortem cortical tissue. Participants were recruited beginning in 1994 (Religious Orders Study) and 1997 (Memory and Aging Project), and followed annually with questionnaires and clinical evaluations; brain specimens were obtained for 80–90% of participants. Mean age at death was 88.0 (SD 6.7) years. We used linear regression, logistic regression, and linear mixed models, to examine relations of epigenetic clock ages to neuropathologic and clinical aging phenotypes, controlling for chronologic age, sex, education, and depressive symptomatology.
Hannum, Horvath, PhenoAge and Cortical clock ages were related to pathologic diagnosis of Alzheimer's disease (AD), as well as to Aβ load (a hallmark pathology of Alzheimer's disease). However, associations were substantially stronger for the Cortical than other clocks; for example, each standard deviation (SD) increase in Hannum, Horvath, and PhenoAge clock age was related to approximately 30% greater likelihood of pathologic AD (all p < 0.05), while each SD increase in Cortical age was related to 90% greater likelihood of pathologic AD (odds ratio = 1.91, 95% confidence interval 1.38, 2.62). Moreover, Cortical age was significantly related to other AD pathology (eg, mean tau tangle density, p = 0.003), and to odds of neocortical Lewy body pathology (for each SD increase in Cortical age, odds ratio = 2.00, 95% confidence 1.27, 3.17), although no clocks were related to cerebrovascular neuropathology. Cortical age was the only epigenetic clock significantly associated with the clinical phenotypes examined, from dementia to cognitive decline (5 specific cognitive systems, and a global cognitive measure averaging 17 tasks) to Parkinsonian signs. Overall, our findings provide evidence of the critical necessity for bespoke clocks of brain aging for advancing research to understand, and eventually prevent, neurodegenerative diseases of aging.
•We calculated five epigenetic clocks in 721 postmortem cortical specimens•Epigenetic clocks trained in blood samples did not predict most neurologic phenotypes•One epigenetic clock, trained in cortex, identified neurodegenerative phenotypes•Findings provide convincing evidence for clocks developed in tissues of interest
Chronic myelomonocytic leukemia (CMML) is a clonal hematopoietic malignancy that is characterized by features of both a myeloproliferative neoplasm and a myelodysplastic syndrome. Thus far, data on a ...comprehensive cytogenetic or molecular genetic characterization are limited.
Here, we analyzed 81 thoroughly characterized patients with CMML (CMML type 1, n = 45; CMML type 2, n = 36) by applying next-generation sequencing (NGS) technology to investigate CBL, JAK2, MPL, NRAS, and KRAS at known mutational hotspot regions. In addition, complete coding regions were analyzed for RUNX1 (beta isoform) and TET2 aberrations.
Cytogenetic aberrations were found in 18.2% of patients (14 of 77 patients). In contrast, at least one molecular mutation was observed in 72.8% of patients (59 of 81 patients). A mean of 1.6 mutations per patient was observed by this unprecedented screening. In total, 105 variances were detected by this comprehensive molecular screening. After excluding known polymorphisms or silent mutations, 82 distinct mutations remained (CBL, n = 15; JAK2V617F, n = 8; MPL, n = 0; NRAS, n = 10; KRAS, n = 12; RUNX1, n = 7; and TET2, n = 41). With respect to clinical data, a better outcome was seen for patients carrying TET2 mutations (P = .013).
The number of molecular markers used to categorize myeloid neoplasms is constantly increasing. Here, NGS screening has been demonstrated to support a comprehensive characterization of the molecular background in CMML. A pattern of molecular mutations translates into different biologic and prognostic categories of CMML.
There is a need for new therapeutic targets with which to prevent Alzheimer's disease (AD), a major contributor to aging-related cognitive decline. Here we report the construction and validation of a ...molecular network of the aging human frontal cortex. Using RNA sequence data from 478 individuals, we first build a molecular network using modules of coexpressed genes and then relate these modules to AD and its neuropathologic and cognitive endophenotypes. We confirm these associations in two independent AD datasets. We also illustrate the use of the network in prioritizing amyloid- and cognition-associated genes for in vitro validation in human neurons and astrocytes. These analyses based on unique cohorts enable us to resolve the role of distinct cortical modules that have a direct effect on the accumulation of AD pathology from those that have a direct effect on cognitive decline, exemplifying a network approach to complex diseases.
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