Downstream Analysis Research Articles

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

A modified CTAB method for the extraction of high-quality RNA from mono-and dicotyledonous plants rich in secondary metabolites

BackgroundHigh-quality RNA extraction from woody plants is difficult because of the presence of polysaccharides and polyphenolics that bind or co-precipitate with the RNA. The CTAB (cetyl trimethylammonium bromide) based method is widely used for the isolation of nucleic acids from polysaccharide-rich plants. Despite the widespread use of the CTAB method, it is necessary to adapt it to particular plant species, tissues and organs. Here we described a simple and generalized method for RNA isolation from mature leaf tissues of several economically important woody (17) and herbaceous plants (2) rich in secondary metabolites. High yields were achieved from small amount (up to 50 mg) of plant material. Two main modifications were applied to the basic protocol: an increase in β-mercaptoethanol concentration (to 10%v/v) and the use of an effective DNase treatment. As opposed to similar studies, we tried to describe a more detailed protocol for isolating RNA, including the exact quantity and concentration of the reagents were used.ResultsOur modified CTAB method is proved to be efficient in extracting the total RNA from a broad range of woody and herbaceous species. The RNA yield was ranged from 2.37 to 91.33 µg/µl. The A260:A280 and A260:A230 absorbance ratios were measured from 1.77 to 2.13 and from 1.81 to 2.22. The RIN value (RNA Integrity Number) of the samples fell between 7.1 and 8.1, which indicated that a small degree of RNA degradation occurred during extraction. The presence of a single peak in the melt curve analyses and low standard errors of the Ct values of replicated measurements indicated the specificity of the primers to bind to the cDNA.ConclusionsOur RNA isolation method, with fine-tuned and detailed instructions, can produce high quality RNA from a small amount of starting plant material that is suitable for use in downstream transcriptional analyses. The use of an increased concentration of the reducing agent β-mercaptoethanol in the extraction buffer, as well as the application of DNaseI-treatment resulted in a method suitable for a wide range of plants without the need of further optimalization, especially in Rhus typhina (Staghorn sumac), for which molecular-genetic studies have not yet been sufficiently explored.

ViroISDC: a method for calling integration sites of hepatitis B virus based on feature encoding

BackgroundHepatitis B virus (HBV) integrates into human chromosomes and can lead to genomic instability and hepatocarcinogenesis. Current tools for HBV integration site detection lack accuracy and stability.ResultsThis study proposes a deep learning-based method, named ViroISDC, for detecting integration sites. ViroISDC generates corresponding grammar rules and encodes the characteristics of the language data to predict integration sites accurately. Compared with Lumpy, Pindel, Seeksv, and SurVirus, ViroISDC exhibits better overall performance and is less sensitive to sequencing depth and integration sequence length, displaying good reliability, stability, and generality. Further downstream analysis of integrated sites detected by ViroISDC reveals the integration patterns and features of HBV. It is observed that HBV integration exhibits specific chromosomal preferences and tends to integrate into cancerous tissue. Moreover, HBV integration frequency was higher in males than females, and high-frequency integration sites were more likely to be present on hepatocarcinogenesis- and anti-cancer-related genes, validating the reliability of the ViroISDC.ConclusionsViroISDC pipeline exhibits superior precision, stability, and reliability across various datasets when compared to similar software. It is invaluable in exploring HBV infection in the human body, holding significant implications for the diagnosis, treatment, and prognosis assessment of HCC.

Phylobook: a tool for display, clade annotation and extraction of sequences from molecular phylogenies.

As the volume of sequence data from variable pathogens increases, means of analyzing, annotating and extracting specific taxa for study becomes more difficult. To meet these challenges for datasets with hundreds to thousands of taxa, 'Phylobook' was developed. Starting with a sequence alignment file, Phylobook generates and displays phylogenetic trees adjacent to highlighter plots showing the position of mutations, and allows the user to identify lineages and recombinants, annotate and export selected subsets of sequences for downstream analysis. Accurate lineage assignment, which is difficult to automate, is aided using annotations created by different clustering methods. Phylobook provides web-based display combined with automated clustering and manual editing to allow for expert assessment and correction of lineage assignments and extraction for downstream analysis.

Deep Neural Networks Based Denoising of Regional Seismic Waveforms and Impact on Analysis of North Korean Nuclear Tests

AbstractWe test a deep learning based denoising autoencoder algorithm on regional and teleseismic seismological and hydroacoustic datasets, which we compile from the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organisation. We focus on stations which can be relevant to investigate North Korean nuclear tests. Denoising of waveform records using autoencoder techniques potentially enables improved signal detection and processing due to lowered signal-to-noise ratios. We train and compare the performance of several different denoising autoencoder models, for short- and long waveform periods, trained on the complete station network as well as on individual stations. We investigate if the denoised waveform signals are useful for seismic source analysis and if they can still be reliably used in downstream analysis for further inferences on the seismic source type, i.e. seismic moment tensor analysis. The declared North Korean nuclear tests are a suitable benchmark test set, as they have extensively been researched and their source type and location might be assumed known. Verification of the source type is of particular interest for potential nuclear tests under international law. We find that care needs to be taken using the denoised waveform data, as a slight bias is introduced in the seismic moment tensor analysis. However we also find promising results hinting at possible future use of the technique for standard analyses, as it improves the investigation of smaller events. Autoencoder based denoising techniques could be employed in future routine frameworks to increase earthquake catalog completeness and possibly aid in detecting smaller potential treaty relevant events.

Abstract PO2-28-08: A spatial transcriptomic study of a triple-negative breast cancer (TNBC) patient-derived xenograft (PDX) model of residual disease refractory to conventional chemotherapy

Abstract TNBC is one of the most aggressive subtypes of breast cancer. Combating chemotherapy resistance is critical to improving quality of care and reducing fatality among TNBC patients. In order to understand the mechanisms of chemoresistance in TNBC tumors, it has been proposed that the spatial interactions between cell types in the tumor microenvironment (TME) could offer insights into the differential response to therapy among tumors, and metastatic potential of certain tumors over others. Thus, this study utilizes spatially resolved transcriptomic (SRT) technology to profile the spatial interactions in the TNBC TME. With the goal of better understanding chemoresistance in TNBC and providing a proof-of-concept for new technology, we have launched a longitudinal SRT study of a TNBC PDX of residual disease before, during, and after adriamycin and cyclophosphamide (AC) treatment (Tx). SRT by 10X Genomics Visium was performed on vehicle, AC-treated residual tumor (21 days post-Tx), and AC-treated regrown tumor (50 days post-Tx when tumors regrew to starting tumor volume). This study is divided into two parts: 1) development of a computational pipeline; 2) spatial colocalization analysis of residual and regrown tumors to understand chemoresistance. PART 1: Due to the lack of specialized tools for processing and sorting xenograft reads from SRT data, we developed the Xenomake pipeline, which combines a xenograft sorting algorithm (Xengsort) and spatial barcode demultiplexing pipeline to assign reads into the host and graft organisms for each spatial spot. RESULTS: Xenomake permits clustering the spatial spots into stroma-rich (enriched for mouse mRNAs), and epithelial-rich (enriched for human mRNAs) regions. We show that Xenomake can find differential cytokine production in the stroma and epithelium. Since PDX data separate the tumor into stroma and epithelium by organism, the pipeline enables fine-tuned downstream analysis such as stromal-stromal and stromal-epithelial interactions. Xenomake is thus generally applicable for SRT involving PDX samples. PART 2: Previously we detailed patterns of tumor regression into a residual tumor state, followed by uncontrolled regrowth in the absence of treatment in multiple PDX models of TNBC. Although using PDX models necessitates immune-compromised mice, several types of stromal populations are present and analyzable in these models. Thus, using organism-assigned reads processed by Xenomake, we computationally inferred the localization pattern of stromal cell types in Visium spots including cancer associated fibroblasts (CAF), macrophages (MP), endothelial cells (ENDO), monocytes, and perivascular-like cells. We compared the spatial localization profiles (SLP) of stromal cell types across samples. With human reads, we computed the spatial pathway activity map (SPAM) for the ~30 HALLMARK pathways across samples. We correlated the patterns of SPAM with stroma cell-type SLP to survey the stroma-epithelial interactions across samples. RESULTS: Stroma cells are generally distributed in the tumor periphery in vehicle and AC50, while in AC21 there is a notable increase in stroma abundance and stroma infiltration within tumor mass. SLP of MP (Cd68+ and Csf1r+) is correlated with ENDO (Pecam1+), and with CAF (Acta2+ and Pdgfrb+). Additionally, all 3 cell types are correlated with Vim and Cd44 expression. Although all samples show enrichment of MP, ENDO, and CAF, they interact differently with pathway activities of adjacent epithelial cells. In vehicle and AC50, the 3 cell types are colocalized with OXPHOS, MYC targets, E2F pathway, while in AC21, a switch to colocalization with EMT is observed. Furthermore, hypoxia response and glycolysis display anti-correlation with stroma cell types, meaning that these pathway activities are further away from stroma and occupy distinct territories. The results suggest stroma-tumor metabolic crosstalk and ways of targeting residual disease. Citation Format: Benjamin Strope, Katherine Pendleton, William Bowie, Gloria Echeverria, Qian Zhu. A spatial transcriptomic study of a triple-negative breast cancer (TNBC) patient-derived xenograft (PDX) model of residual disease refractory to conventional chemotherapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-28-08.

SAN: Mitigating spatial covariance heterogeneity in cortical thickness data collected from multiple scanners or sites.

In neuroimaging studies, combining data collected from multiple study sites or scanners is becoming common to increase the reproducibility of scientific discoveries. At the same time, unwanted variations arise by using different scanners (inter-scanner biases), which need to be corrected before downstream analyses to facilitate replicable research and prevent spurious findings. While statistical harmonization methods such as ComBat have become popular in mitigating inter-scanner biases in neuroimaging, recent methodological advances have shown that harmonizing heterogeneous covariances results in higher data quality. In vertex-level cortical thickness data, heterogeneity in spatial autocorrelation is a critical factor that affects covariance heterogeneity. Our work proposes a new statistical harmonization method called spatial autocorrelation normalization (SAN) that preserves homogeneous covariance vertex-level cortical thickness data across different scanners. We use an explicit Gaussian process to characterize scanner-invariant and scanner-specific variations to reconstruct spatially homogeneous data across scanners. SAN is computationally feasible, and it easily allows the integration of existing harmonization methods. We demonstrate the utility of the proposed method using cortical thickness data from the Social Processes Initiative in the Neurobiology of the Schizophrenia(s) (SPINS) study. SAN is publicly available as an R package.

YBX1, Targeted By Microrna-382-5p, Promotes Laryngeal Squamous Cell Carcinoma Progression via Modulating RAS/MAPK Signaling.

Laryngeal squamous cell carcinoma (LSCC) is the most common cancer of head and neck cancer. Y-box binding protein-1 (YBX1) has tumor-promoting effects in some types of cancers. However, its role in LSCC remains unknown. This study set out to identify the role of YBX1 in LSCC. Bioinformatics analysis of the Gene Expression Omnibus (GEO) database and our cohort data were used to explore the association of YBX1 expression with clinicopathological factors in LSCC. Then, cells with stably or transiently transfected with plasmid or siRNA were constructed to assess the effect of loss and gain of YBX1 on the biological phenotypes of LSCC cells in vitro. In addition, subcutaneous xenograft and orthotopic liver tumor mouse models were constructed for validation. The interrogated miRNA databases and subsequent luciferase reporter assays were used to confirm the miR-382-5p target of YBX1. At last, KEGG enrichment annotation from TGCA data was used for downstream analyses of miR-382-5p/YBX1 and verified by PCR and Western immunoblotting. The results showed that significant upregulation of YBX1 in LSCC tumors was correlated with advanced TNM stage and poor prognosis. Knockdown of YBX1 markedly impaired the proliferative, invasive, and migratory activity of Tu212 cells. We confirmed that miR-382-5p targets YBX1 to mediate LSCC progression both in vitro and in vivo. We further confirmed that miR-382-5p/YBX1 modulated the Ras/MAPK signaling axis to regulate the progression of LSCC. Together, our results indicated that YBX1 is an important promoter of LSCC progression. And miR-382-5p/YBX1/RAS/MAPK signaling pathway can be perceived as a promising target in the treatment of LSCC.

Разработка и реализация протокола поведенческого эксперимента для оценки когнитивных способностей и социального поведения мышей после стресса в раннем возрасте

<p style="text-align: justify;">This manuscript presents a protocol designed for the comprehensive investigation of early life stress (ELS) outcomes and a feasibility study conducted with this protocol. ELS alters normal development by interfering at various levels: hormonal changes, brain cellular architecture, epigenome, and chromosomal structural elements. The protocol combines classic behavioral tests with advanced molecular techniques to obtain comprehensive data and thus uncover the underlying mechanisms of ELS. In this protocol, the main source of stress is maternal separation. Briefly, a group of C57Bl/6 mice undergoes maternal separation; then, mice perform the radial maze test and the resident-intruder test. As a control, another group of mice stays undisturbed and performs the same behavioral tests in the same timeframe. After the behavioral tests, biosamples are collected, including urine for corticosterone measurements, peripheral blood, hippocampus, amygdala, and prefrontal cortex tissues for DNA isolation and its downstream analyses (DNA methylation profiling and telomere length measuring), and whole brains for immunohistochemistry analysis of the glucocorticoid receptor density. This protocol was successfully tested as a feasibility study for a large-scale investigation that addresses potential flaws to establish a robust methodology. This paper reports on a comprehensive approach to examining multiple aspects of development that interrogates a holistic analysis of multilayer and multidimensional data and may contribute valuable insights for both animal and human studies.</p>

Identifying the Trends of Urinary microRNAs within Extracellular Vesicles for Esophageal Cancer

Background: The advancement of multidisciplinary treatment has increased the need to develop tests to monitor tumor burden during treatment. We herein analyzed urinary microRNAs within extracellular vesicles from patients with esophageal squamous cell carcinoma (ESCC) and normal individuals using a microarray. Methods: Patients with advanced ESCC who underwent esophagectomy (A), endoscopic submucosal resection (ESD) (B), and healthy donors (C) were included. Based on microRNA expression among the groups (Analysis 1), microRNAs with significant differences between groups A and C were selected (Analysis 2). Of these candidates, microRNAs in which the change between A and C was consistent with the change between B and C were selected for downstream analysis (Analysis 3). Finally, microRNA expression was validated in patients with recurrence from A (exploratory analysis). Results: For analysis 1, 205 microRNAs were selected. For Analyses 2 and 3, the changes in 18 microRNAs were consistent with changes in tumor burden as determined by clinical imaging and pathological findings. The AUC for the detection of ESCC using 18 microRNAs was 0.72. In exploratory analysis, three of eighteen microRNAs exhibited a concordant trend with recurrence. Conclusions: The current study identified the urinary microRNAs which were significantly expressed in ESCC patients. Validation study is warranted to evaluate whether these microRNAs could reflect tumor burden during multidisciplinary treatment for ESCC.

Revealing Genetic Dynamics: scRNA-seq Unravels Modifications in Human PDL Cells across In Vivo and In Vitro Environments

The periodontal ligament (PDL) is a highly specialized fibrous tissue comprising heterogeneous cell populations of an intricate nature. These complexities, along with challenges due to cell culture, impede a comprehensive understanding of periodontal pathophysiology. This study aims to address this gap, employing single-cell RNA sequencing (scRNA-seq) technology to analyze the genetic intricacies of PDL both in vivo and in vitro. Primary human PDL samples (n = 7) were split for direct in vivo analysis and cell culture under serum-containing and serum-free conditions. Cell hashing and sorting, scRNA-seq library preparation using the 10x Genomics protocol, and Illumina sequencing were conducted. Primary analysis was performed using Cellranger, with downstream analysis via the R packages Seurat and SCORPIUS. Seven distinct PDL cell clusters were identified comprising different cellular subsets, each characterized by unique genetic profiles, with some showing donor-specific patterns in representation and distribution. Formation of these cellular clusters was influenced by culture conditions, particularly serum presence. Furthermore, certain cell populations were found to be inherent to the PDL tissue, while others exhibited variability across donors. This study elucidates specific genes and cell clusters within the PDL, revealing both inherent and context-driven subpopulations. The impact of culture conditions—notably the presence of serum—on cell cluster formation highlights the critical need for refining culture protocols, as comprehending these influences can drive the creation of superior culture systems vital for advancing research in PDL biology and regenerative therapies. These discoveries not only deepen our comprehension of PDL biology but also open avenues for future investigations into uncovering underlying mechanisms.

Implications of gene tree heterogeneity on downstream phylogenetic analyses: A case study employing the Fair Proportion index.

Many questions in evolutionary biology require the specification of a phylogeny for downstream phylogenetic analyses. However, with the increasingly widespread availability of genomic data, phylogenetic studies are often confronted with conflicting signal in the form of genomic heterogeneity and incongruence between gene trees and the species tree. This raises the question of determining what data and phylogeny should be used in downstream analyses, and to what extent the choice of phylogeny (e.g., gene trees versus species trees) impacts the analyses and their outcomes. In this paper, we study this question in the realm of phylogenetic diversity indices, which provide ways to prioritize species for conservation based on their relative evolutionary isolation on a phylogeny, and are thus one example of downstream phylogenetic analyses. We use the Fair Proportion (FP) index, also known as the evolutionary distinctiveness score, and explore the variability in species rankings based on gene trees as compared to the species tree for several empirical data sets. Our results indicate that prioritization rankings among species vary greatly depending on the underlying phylogeny, suggesting that the choice of phylogeny is a major influence in assessing phylogenetic diversity in a conservation setting. While we use phylogenetic diversity conservation as an example, we suspect that other types of downstream phylogenetic analyses such as ancestral state reconstruction are similarly affected by genomic heterogeneity and incongruence. Our aim is thus to raise awareness of this issue and inspire new research on which evolutionary information (species trees, gene trees, or a combination of both) should form the basis for analyses in these settings.

MiR-21, miR-221, miR-29 and miR-34 are distinguishable molecular features of a metabolically unhealthy phenotype in young adults.

Discrepancies between the measurement of body mass index (BMI) and metabolic health status have been described for the onset of metabolic diseases. Studying novel biomarkers, some of which are associated with metabolic syndrome, can help us to understand the differences between metabolic health (MetH) and BMI. A group of 1469 young adults with pre-specified anthropometric and blood biochemical parameters were selected. Of these, 80 subjects were included in the downstream analysis that considered their BMI and MetH parameters for selection as follows: norm weight metabolically healthy (MHNW) or metabolically unhealthy (MUNW); overweight/obese metabolically healthy (MHOW) or metabolically unhealthy (MUOW). Our results showed for the first time the differences when the MetH status and the BMI are considered as global MetH statures. First, all the evaluated miRNAs presented a higher expression in the metabolically unhealthy group than the metabolically healthy group. The higher levels of leptin, IL-1b, IL-8, IL-17A, miR-221, miR-21, and miR-29 are directly associated with metabolic unhealthy and OW/OB phenotypes (MUOW group). In contrast, high levels of miR34 were detected only in the MUNW group. We found differences in the SIRT1-PGC1α pathway with increased levels of SIRT1+ cells and diminished mRNA levels of PGCa in the metabolically unhealthy compared to metabolically healthy subjects. Our results demonstrate that even when metabolic diseases are not apparent in young adult populations, MetH and BMI have a distinguishable phenotype print that signals the potential to develop major metabolic diseases.

Joint Bayesian estimation of cell dependence and gene associations in spatially resolved transcriptomic data

Recent technologies such as spatial transcriptomics, enable the measurement of gene expressions at the single-cell level along with the spatial locations of these cells in the tissue. Spatial clustering of the cells provides valuable insights into the understanding of the functional organization of the tissue. However, most such clustering methods involve some dimension reduction that leads to a loss of the inherent dependency structure among genes at any spatial location in the tissue. This destroys valuable insights of gene co-expression patterns apart from possibly impacting spatial clustering performance. In spatial transcriptomics, the matrix-variate gene expression data, along with spatial coordinates of the single cells, provides information on both gene expression dependencies and cell spatial dependencies through its row and column covariances. In this work, we propose a joint Bayesian approach to simultaneously estimate these gene and spatial cell correlations. These estimates provide data summaries for downstream analyses. We illustrate our method with simulations and analysis of several real spatial transcriptomic datasets. Our work elucidates gene co-expression networks as well as clear spatial clustering patterns of the cells. Furthermore, our analysis reveals that downstream spatial-differential analysis may aid in the discovery of unknown cell types from known marker genes.

Shaoxia: a web-based interactive analysis platform for single cell RNA sequencing data

BackgroundIn recent years, Single-cell RNA sequencing (scRNA-seq) is increasingly accessible to researchers of many fields. However, interpreting its data demands proficiency in multiple programming languages and bioinformatic skills, which limited researchers, without such expertise, exploring information from scRNA-seq data. Therefore, there is a tremendous need to develop easy-to-use software, covering all the aspects of scRNA-seq data analysis.ResultsWe proposed a clear analysis framework for scRNA-seq data, which emphasized the fundamental and crucial roles of cell identity annotation, abstracting the analysis process into three stages: upstream analysis, cell annotation and downstream analysis. The framework can equip researchers with a comprehensive understanding of the analysis procedure and facilitate effective data interpretation. Leveraging the developed framework, we engineered Shaoxia, an analysis platform designed to democratize scRNA-seq analysis by accelerating processing through high-performance computing capabilities and offering a user-friendly interface accessible even to wet-lab researchers without programming expertise.ConclusionShaoxia stands as a powerful and user-friendly open-source software for automated scRNA-seq analysis, offering comprehensive functionality for streamlined functional genomics studies. Shaoxia is freely accessible at http://www.shaoxia.cloud, and its source code is publicly available at https://github.com/WiedenWei/shaoxia.

DIMet: An open-source tool for Differential analysis of targeted Isotope-labeled Metabolomics data.

Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of Stable Isotope-Resolved Metabolomics data, there is currently no available resource providing a comprehensive toolbox. In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms. DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786. Supplementary data are available at Bioinformatics online.

K-nearest-neighbors induced topological PCA for single cell RNA-sequence data analysis

Single-cell RNA sequencing (scRNA-seq) is widely used to reveal heterogeneity in cells, which has given us insights into cell–cell communication, cell differentiation, and differential gene expression. However, analyzing scRNA-seq data is a challenge due to sparsity and the large number of genes involved. Therefore, dimensionality reduction and feature selection are important for removing spurious signals and enhancing downstream analysis. Traditional PCA, a main workhorse in dimensionality reduction, lacks the ability to capture geometrical structure information embedded in the data, and previous graph Laplacian regularizations are limited by the analysis of only a single scale. We propose a topological Principal Components Analysis (tPCA) method by the combination of persistent Laplacian (PL) technique and L2,1 norm regularization to address multiscale and multiclass heterogeneity issues in data. We further introduce a k-Nearest-Neighbor (kNN) persistent Laplacian technique to improve the robustness of our persistent Laplacian method. The proposed kNN-PL is a new algebraic topology technique which addresses the many limitations of the traditional persistent homology. Rather than inducing filtration via the varying of a distance threshold, we introduced kNN-tPCA, where filtrations are achieved by varying the number of neighbors in a kNN network at each step, and find that this framework has significant implications for hyper-parameter tuning. We validate the efficacy of our proposed tPCA and kNN-tPCA methods on 11 diverse benchmark scRNA-seq datasets, and showcase that our methods outperform other unsupervised PCA enhancements from the literature, as well as popular Uniform Manifold Approximation (UMAP), t-Distributed Stochastic Neighbor Embedding (tSNE), and Projection Non-Negative Matrix Factorization (NMF) by significant margins. For example, tPCA provides up to 628%, 78%, and 149% improvements to UMAP, tSNE, and NMF, respectively on classification in the F1 metric, and kNN-tPCA offers 53%, 63%, and 32% improvements to UMAP, tSNE, and NMF, respectively on clustering in the ARI metric.

Multimodal joint deconvolution and integrative signature selection in proteomics

Deconvolution is an efficient approach for detecting cell-type-specific (cs) transcriptomic signals without cellular segmentation. However, this type of methods may require a reference profile from the same molecular source and tissue type. Here, we present a method to dissect bulk proteome by leveraging tissue-matched transcriptome and proteome without using a proteomics reference panel. Our method also selects the proteins contributing to the cellular heterogeneity shared between bulk transcriptome and proteome. The deconvoluted result enables downstream analyses such as cs-protein Quantitative Trait Loci (cspQTL) mapping. We benchmarked the performance of this multimodal deconvolution approach through CITE-seq pseudo bulk data, a simulation study, and the bulk multi-omics data from human brain normal tissues and breast cancer tumors, individually, showing robust and accurate cell abundance quantification across different datasets. This algorithm is implemented in a tool MICSQTL that also provides cspQTL and multi-omics integrative visualization, available at https://bioconductor.org/packages/MICSQTL.

Optimized Method for High‐Quality RNA Extraction from Formalin‐Fixed Paraffin‐Embedded Tissues

AbstractRNA extraction from formalin‐fixed paraffin‐embedded tissues (FFPE) presents a notable challenge. Extracting RNA from FFPE tissues has been challenging due to RNA degradation and chemical bonding between RNA and proteins during the fixation process. This results in reduced concentration and quality of the extracted RNA from FFPE tissues, impacting subsequent downstream analyses. In this study, we present an optimized approach involves replacing the tissue temperature‐digestion step with tissue crushing and homogenization. RNA extraction was performed on 100 FFPE tissue blocks obtained from the archive of Pathology Department of Shiraz University of Medical Sciences. After deparafinization of the tissues, RNA was extracted using this modified method, and the results compared to those of a standard extraction procedure and commercial kits used for FFPE tissue. Then gel electrophoresis and Real time PCR were done to check the quality and integrity of the extracted RNA. The results show that the concentration of RNA obtained from this method was significantly (P value <0.0001) higher than the other methods. In conclusion, we have successfully introduced a modified method that enables the extraction of high‐quality and intact RNA from FFPE tissue. This method stands as a reliable option for molecular biology studies necessitating precise RNA extraction.

A roadmap of phylogenomic methods for studying polyploid plant genera

AbstractPhylogenetic inference of polyploid species is the first step towards understanding their patterns of diversification. In this paper, we review the challenges and limitations of inferring species relationships of polyploid plants using traditional phylogenetic sequencing approaches, as well as the mischaracterization of the species tree from single or multiple gene trees. We provide a roadmap to infer interspecific relationships among polyploid lineages by comparing and evaluating the application of current phylogenetic, phylogenomic, transcriptomic, and whole‐genome approaches using different sequencing platforms. For polyploid species tree reconstruction, we assess the following criteria: (1) the amount of prior information or tools required to capture the genetic region(s) of interest; (2) the probability of recovering homeologs for polyploid species; and (3) the time efficiency of downstream data analysis. Moreover, we discuss bioinformatic pipelines that can reconstruct networks of polyploid species relationships. In summary, although current phylogenomic approaches have improved our understanding of reticulate species relationships in polyploid‐rich genera, the difficulties of recovering reliable orthologous genes and sorting all homeologous copies for allopolyploids remain a challenge. In the future, assembled long‐read sequencing data will assist the recovery and identification of multiple gene copies, which can be particularly useful for reconstructing the multiple independent origins of polyploids.