When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test. 

/pdf/exaggerated-false-positives-by-popular-differential-5toeatjp.pdf

Exaggerated false positives by popular differential expression methods when analyzing human population samples

Distinct transcriptome architectures underlying lupus establishment and exacerbation

Conserved genomic sequences disrupted in humans may underlie uniquely human phenotypic traits. We identified and characterized 10,032 human-specific conserved deletions (hCONDELs). These short (average 2.56 base pairs) deletions are enriched for human brain functions across genetic, epigenomic, and transcriptomic datasets. Using massively parallel reporter assays in six cell types, we discovered 800 hCONDELs conferring significant differences in regulatory activity, half of which enhance rather than disrupt regulatory function. We highlight several hCONDELs with putative human-specific effects on brain development, including HDAC5, CPEB4, and PPP2CA. Reverting an hCONDEL to the ancestral sequence alters the expression of LOXL2 and developmental genes involved in myelination and synaptic function. Our data provide a rich resource to investigate the evolutionary mechanisms driving new traits in humans and other species. Description INTRODUCTION Deciphering the molecular and genetic changes that differentiate humans from our closest primate relatives is critical for understanding our origins. Although earlier studies have prioritized how newly gained genetic sequences or variations have contributed to evolutionary innovation, the role of sequence loss has been less appreciated. Alterations in evolutionary conserved regions that are enriched for biological function could be particularly more likely to have phenotypic effects. We thus sought to identify and characterize sequences that have been conserved across evolution, but are then surprisingly lost in all humans. These human-specific deletions in conserved regions (hCONDELs) may play an important role in uniquely human traits. RATIONALE Sequencing advancements have identified millions of genetic changes between chimpanzee and human genomes; however, the functional impacts of the ~1 to 5% difference between our species is largely unknown. hCONDELs are one class of these predominantly noncoding sequence changes. Although large hCONDELs (>1 kb) have been previously identified, the vast majority of all hCONDELs (95.7%) are small (<20 base pairs) and have not yet been functionally assessed. We adapted massively parallel reporter assays (MPRAs) to characterize the effects of thousands of these small hCONDELs and uncovered hundreds with functional effects. By understanding the effects of these hCONDELs, we can gain insight into the mechanistic patterns driving evolution in the human genome. RESULTS We identified 10,032 hCONDELs by examining conserved regions across diverse vertebrate genomes and overlapping with confidently annotated, human-specific fixed deletions. We found that these hCONDELs are enriched to delete conserved sequences originating from stem amniotes. Overlap with transcriptional, epigenomic, and phenotypic datasets all implicate neuronal and cognitive functional impacts. We characterized these hCONDELs using MPRA in six different human cell types, including induced pluripotent stem cell–derived neural progenitor cells. We found that 800 hCONDELs displayed species-specific regulatory effect effects. Although many hCONDELs perturb transcription factor–binding sites in active enhancers, we estimate that 30% create or improve binding sites, including activators and repressors. Some hCONDELs exhibit molecular functions that affect core neurodevelopmental genes. One hCONDEL removes a single base in an active enhancer in the neurogenesis gene HDAC5, and another deletes six bases in an alternative promoter of PPP2CA, a gene that regulates neuronal signaling. We deeply characterized an hCONDEL in a putative regulatory element of LOXL2, a gene that controls neuronal differentiation. Using genome engineering to reintroduce the conserved chimpanzee sequence into human cells, we confirmed that the human deletion alters transcriptional output of LOXL2. Single-cell RNA sequencing of these cells uncovered a cascade of myelination and synaptic function–related transcriptional changes induced by the hCONDEL. CONCLUSION Our identification of hundreds of hCONDELs with functional impacts reveals new molecular changes that may have shaped our unique biological lineage. These hCONDELs display predicted functions in a variety of biological systems but are especially enriched for function in neuronal tissue. Many hCONDELs induced gains of regulatory activity, a surprising discovery given that deletions of conserved bases are commonly thought to abrogate function. Our work provides a paradigm for the characterization of nucleotide changes shaping species-specific biology across humans or other animals. Human-specific deletions that remove nucleotides from regions highly conserved in other animals (hCONDELs). We assessed 10,032 hCONDELs across diverse, biologically relevant datasets and identified tissue-specific enrichment (top left). The regulatory impact of hCONDELs was characterized by comparing chimp and human sequences in MPRAs (bottom left). The ability of hCONDELs to either improve or perturb activating and repressing gene-regulatory elements was assessed (top right). The deleted chimpanzee sequence was reintroduced back into human cells, causing a cascade of transcriptional differences for an hCONDEL regulating LOXL2 (bottom right).

The functional and evolutionary impacts of human-specific deletions in conserved elements

Abstract Background Alzheimer’s disease (AD), a progressive neurodegenerative disease, is the most common cause of dementia worldwide. Accumulating data support the contributions of the peripheral immune system in AD pathogenesis. However, there is a lack of comprehensive understanding about the molecular characteristics of peripheral immune cells in AD. Methods To explore the alterations of cellular composition and the alterations of intrinsic expression of individual cell types in peripheral blood, we performed cellular deconvolution in a large-scale bulk blood expression cohort and identified cell-intrinsic differentially expressed genes in individual cell types with adjusting for cellular proportion. Results We detected a significant increase and decrease in the proportion of neutrophils and B lymphocytes in AD blood, respectively, which had a robust replicability across other three AD cohorts, as well as using alternative algorithms. The differentially expressed genes in AD neutrophils were enriched for some AD-associated pathways, such as ATP metabolic process and mitochondrion organization. We also found a significant enrichment of protein-protein interaction network modules of leukocyte cell-cell activation, mitochondrion organization, and cytokine-mediated signaling pathway in neutrophils for AD risk genes including CD33 and IL1B . Both changes in cellular composition and expression levels of specific genes were significantly associated with the clinical and pathological alterations. A similar pattern of perturbations on the cellular proportion and gene expression levels of neutrophils could be also observed in mild cognitive impairment (MCI). Moreover, we noticed an elevation of neutrophil abundance in the AD brains. Conclusions We revealed the landscape of molecular perturbations at the cellular level for AD. These alterations highlight the putative roles of neutrophils in AD pathobiology. 

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Cellular transcriptional alterations of peripheral blood in Alzheimer’s disease

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications. 

/pdf/data-analysis-guidelines-for-single-cell-rna-seq-in-1anmtiaz.pdf

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

https://genomebiology.biomedcentral.com/track/pdf/10.1186/s13059-022-02648-4

Abstract Background Estimating and accounting for hidden variables is widely practiced as an important step in molecular quantitative trait locus (molecular QTL, henceforth “QTL”) analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose. Results Here we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)—a well-established dimension reduction and factor discovery method—via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better-performing, and much easier to interpret and use. Conclusions To help researchers use PCA in their QTL analysis, we provide an R package along with a detailed guide, both of which are freely available at https://github.com/heatherjzhou/PCAForQTL . We believe that using PCA rather than SVA, PEER, or HCP will substantially improve and simplify hidden variable inference in QTL mapping as well as increase the transparency and reproducibility of QTL research. 

PCA outperforms popular hidden variable inference methods for molecular QTL mapping

In this response to the correspondence by Hejblum et al. [1], we clarify the reasons why we ran the Wilcoxon rank-sum test on the semi-synthetic RNA-seq samples without normalization, and why we could only run dearseq with its built-in normalization, in our published study [2]. We also argue that no normalization should be performed on the semi-synthetic samples. Hence, for a fairer method comparison and using the updated dearseq package by Hejblum et al., we re-run the six differential expression methods (DESeq2, edgeR, limma-voom, dearseq, NOISeq, and the Wilcoxon rank-sum test) without normalizing the semi-synthetic samples, i.e., under the “No normalization” scheme in [1]. Our updated results show that the Wilcoxon rank-sum test is still the best method in terms of false discovery rate (FDR) control and power performance under all settings investigated.

Wilcoxon rank-sum test still outperforms dearseq after accounting for the normalization impact in semi-synthetic RNA-seq data simulation

Methylation of the p16 promoter resulting in epigenetic gene silencing-known as p16 epimutation-is frequently found in human colorectal cancer and is also common in normal-appearing colonic mucosa of aging individuals. Thus, to improve clinical care of colorectal cancer (CRC) patients, we explored the role of age-related p16 epimutation in intestinal tumorigenesis.We established a mouse model that replicates two common genetic and epigenetic events observed in human CRCs: Apc mutation and p16 epimutation. We conducted long-term survival and histological analysis of tumor development and progression. Colonic epithelial cells and tumors were collected from mice and analyzed by RNA sequencing (RNA-seq), quantitative PCR, and flow cytometry. We performed single-cell RNA sequencing (scRNA-seq) to characterize tumor-infiltrating immune cells throughout tumor progression. We tested whether anti-PD-L1 immunotherapy affects overall survival of tumor-bearing mice and whether inhibition of both epigenetic regulation and immune checkpoint is more efficacious.Mice carrying combined Apc mutation and p16 epimutation had significantly shortened survival and increased tumor growth compared to those with Apc mutation only. Intriguingly, colon tumors with p16 epimutation exhibited an activated interferon pathway, increased expression of programmed death-ligand 1 (Pdl1), and enhanced infiltration of immune cells. scRNA-seq further revealed the presence of Foxp3+ Tregs and γδT17 cells, which contribute to an immunosuppressive tumor microenvironment (TME). Furthermore, we showed that a combined therapy using an inhibitor of DNA methylation and a PD-L1 immune checkpoint inhibitor is more effective for improving survival in tumor-bearing mice than blockade of either pathway alone.Our study demonstrated that age-dependent p16 epimutation creates a permissive microenvironment for malignant transformation of polyps to colon cancer. Our findings provide a mechanistic rationale for future targeted therapy in patients with p16 epimutation. 

/pdf/functional-characterization-of-age-dependent-p16-epimutation-35oedwsn.pdf

Yumei Li

Papers

Exaggerated false positives by popular differential expression methods when analyzing human population samples

Exaggerated false positives by popular differential expression methods when analyzing human population samples

PCA outperforms popular hidden variable inference methods for molecular QTL mapping

Wilcoxon rank-sum test still outperforms dearseq after accounting for the normalization impact in semi-synthetic RNA-seq data simulation

Functional characterization of age-dependent p16 epimutation reveals biological drivers and therapeutic targets for colorectal cancer