Downstream Analysis Methods Research Articles

A new microscopy pipeline for studying the initial stages of nuclear and micronuclear rupture and repair.

Nuclear envelope repair is a fundamental cellular response to stress, especially for cells experiencing frequent nuclear ruptures, such as cancer cells. Moreover, for chromosomally unstable cancer cells, characterized by the presence of micronuclei, the irreversible rupture of these structures constitutes a fundamental step toward cancer progression and therapy resistance. For these reasons, the study of nuclear envelope rupture and repair is of paramount importance. Nonetheless, due to the constraint imposed by the stochastic nature of rupture events, a precise characterization of the initial stage of nuclear repair remains elusive. In this study, we overcame this limitation by developing a new imaging pipeline that deterministically induces rupture while simultaneously imaging fluorescently tagged repair proteins. We provide a detailed step-by-step protocol to implement this method on any confocal microscope and applied it to study the major nuclear repair protein, barrier-to-autointegration factor (BAF). As a proof of principle, we demonstrated two different downstream analysis methods and showed how BAF is differentially recruited at sites of primary and micronuclear rupture. Additionally, we applied this method to study the recruitment at primary nuclei of the inner nuclear membrane protein LEM-domain 2 (LEMD2) and Charged Multivesicular Protein 7 (CHMP7), the scaffolding protein of the endosomal sorting complex required for transport III (ESCRT-III) membrane remodeling complex. The CHMP7-LEMD2 binding is the fundamental step allowing the recruitment of ESCRT-III, which represents the other major nuclear repair mechanism. This demonstrates the method's applicability for investigating protein dynamics at sites of nuclear and micronuclear envelope rupture and paves the way to more time-resolved studies of nuclear envelope repair.

Parsnp 2.0: scalable core-genome alignment for massive microbial datasets.

Since 2016, the number of microbial species with available reference genomes in NCBI has more than tripled. Multiple genome alignment, the process of identifying nucleotides across multiple genomes which share a common ancestor, is used as the input to numerous downstream comparative analysis methods. Parsnp is one of the few multiple genome alignment methods able to scale to the current era of genomic data; however, there has been no major release since its initial release in 2014. To address this gap, we developed Parsnp v2, which significantly improves on its original release. Parsnp v2 provides users with more control over executions of the program, allowing Parsnp to be better tailored for different use-cases. We introduce a partitioning option to Parsnp, which allows the input to be broken up into multiple parallel alignment processes which are then combined into a final alignment. The partitioning option can reduce memory usage by over 4× and reduce runtime by over 2×, all while maintaining a precise core-genome alignment. The partitioning workflow is also less susceptible to complications caused by assembly artifacts and minor variation, as alignment anchors only need to be conserved within their partition and not across the entire input set. We highlight the performance on datasets involving thousands of bacterial and viral genomes. Parsnp v2 is available at https://github.com/marbl/parsnp.

Standardization of ultrasound images across various centers: M2O-DiffGAN bridging the gaps among unpaired multi-domain ultrasound images

Domain shift problem is commonplace for ultrasound image analysis due to difference imaging setting and diverse medical centers, which lead to poor generalizability of deep learning-based methods. Multi-Source Domain Transformation (MSDT) provides a promising way to tackle the performance degeneration caused by the domain shift, which is more practical and challenging compared to conventional single-source transformation tasks. An effective unsupervised domain combination strategy is highly required to handle multiple domains without annotations. Fidelity and quality of generated images are also important to ensure the accuracy of computer-aided diagnosis. However, existing MSDT approaches underperform in above two areas. In this paper, an efficient domain transformation model named M2O-DiffGAN is introduced to achieve a unified mapping from multiple unlabeled source domains to the target domain. A cycle-consistent “many-to-one” adversarial learning architecture is introduced to model various unlabeled domains jointly. A condition adversarial diffusion process is employed to generate images with high-fidelity, combining an adversarial projector to capture reverse transition probabilities over large step sizes for accelerating sampling. Considering the limited perceptual information of ultrasound images, an ultrasound-specific content loss helps to capture more perceptual features for synthesizing high-quality ultrasound images. Massive comparisons on six clinical datasets covering thyroid, carotid and breast demonstrate the superiority of the M2O-DiffGAN in the performance of bridging the domain gaps and enlarging the generalization of downstream analysis methods compared to state-of-the-art algorithms. It improves the mean MI, Bhattacharyya Coefficient, dice and IoU assessments by 0.390, 0.120, 0.245 and 0.250, presenting promising clinical applications.

Analyzing histone ChIP-seq data with a bin-based probability of being signal.

Histone ChIP-seq is one of the primary methods for charting the cellular epigenomic landscape, the components of which play a critical regulatory role in gene expression. Analyzing the activity of regulatory elements across datasets and cell types can be challenging due to shifting peak positions and normalization artifacts resulting from, for example, differing read depths, ChIP efficiencies, and target sizes. Moreover, broad regions of enrichment seen in repressive histone marks often evade detection by commonly used peak callers. Here, we present a simple and versatile method for identifying enriched regions in ChIP-seq data that relies on estimating a gamma distribution fit to non-overlapping 5kB genomic bins to establish a global background. We use this distribution to assign a probability of being signal (PBS) between zero and one to each 5 kB bin. This approach, while lower in resolution than typical peak-calling methods, provides a straightforward way to identify enriched regions and compare enrichments among multiple datasets, by transforming the data to values that are universally normalized and can be readily visualized and integrated with downstream analysis methods. We demonstrate applications of PBS for both broad and narrow histone marks, and provide several illustrations of biological insights which can be gleaned by integrating PBS scores with downstream data types.

Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments

Spatial proteomics technologies have revealed an underappreciated link between the location of cells in tissue microenvironments and the underlying biology and clinical features, but there is significant lag in the development of downstream analysis methods and benchmarking tools. Here we present SPIAT (spatial image analysis of tissues), a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial simulator), a simulator of tissue spatial data. SPIAT includes multiple colocalization, neighborhood and spatial heterogeneity metrics to characterize the spatial patterns of cells. Ten spatial metrics of SPIAT are benchmarked using simulated data generated with spaSim. We show how SPIAT can uncover cancer immune subtypes correlated with prognosis in cancer and characterize cell dysfunction in diabetes. Our results suggest SPIAT and spaSim as useful tools for quantifying spatial patterns, identifying and validating correlates of clinical outcomes and supporting method development.

Abstract LB056: Crowdsourcing rare cancer research in the Hack4NF GENIE-NF tumor identification and classification challenge

Abstract A key challenge in rare tumor research is the paucity of genomic data that can be used to understand and devise better therapeutic strategies for rare cancers. Furthermore, the “curse of dimensionality,” in which data has many features, such as genetic variants, but few specimens, makes it difficult or impossible to use conventional machine learning techniques to explore these data. To address these challenges in the context of tumors associated with the rare disease neurofibromatosis, we ran a hackathon to stimulate the development of methods to better understand the biology of tumors related to this disease. The hackathon had three challenges centered around variant effect prediction, drug discovery, and genomics. The genomics track of the hackathon leveraged the AACR Project GENIE database (1) and challenged participants to develop new frameworks that accurately use GENIE data to classify neurofibromatosis-related tumors. They were asked to first identify the neurofibromatosis-related tumors in the dataset. They were then asked to use one or more novel classification methods to classify the tumor samples into different groups based on genetic features. To help them do this, we provided access to version 13 of the GENIE database to the hackathon participants, though they were allowed to integrate other relevant datasets. The expected output was a classification method that differentiates different types of NF1, NF2, and schwannomatosis-related tumors using clinical sequencing data, as well as a list of the most important features in the algorithm for differentiating tumor types. Domain expert judges qualitatively scored each team’s rationale for defining and including “NF-related tumors” in their project, and scored the feature list based on the presence of known important biomarkers and features in NF tumors as well as potentially novel features that the algorithm identified. A technical judge also scored the code repository based on documentation and clarity of code. Two teams from the GENIE subchallenge were awarded prizes - Team Next GeNLP as the best overall GENIE challenge submission, and team “Artificial Intelligence for neurofibromatosis” for best project documentation. Both winning teams used methods based on natural language processing (NLP) techniques to reduce the dimensionality and complexity of the variant data, and to identify new representations of NF-relevant tumors, and then applied downstream analysis methods such as distance calculations and feature prioritization to better understand the genomic profiles of different tumors. While these methods and tools focused on NF-specific tumor types, we anticipate that they could be re-used by others to better explore the biology and interrelatedness of other rare tumors within the GENIE database. (1) The AACR Project GENIE Consortium. AACR Project GENIE: Powering Precision Medicine Through An International Consortium, Cancer Discov. 2017. The authors would like to acknowledge the American Association for Cancer Research and its financial and material support in the development of the AACR Project GENIE registry, as well as members of the consortium for their commitment to data sharing. Interpretations are the responsibility of study authors. Citation Format: Robert J. Allaway, Sasha Scott, Gabriel Altay, Hariprasad Donthi, Karthika R, Muhammad Alaa Alwattar, Lucas Pastur Romay, Ayesha Parsha, Jineta Banerjee, Chelsea Nayan, Julie Bletz, Salvatore La Rosa. Crowdsourcing rare cancer research in the Hack4NF GENIE-NF tumor identification and classification challenge [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB056.

Multiplex Tissue Imaging: Spatial Revelations in the Tumor Microenvironment

Simple SummaryCancer is the leading cause of death worldwide, and the overall aging of the population results in an increased risk of a cancer diagnosis during a person’s lifetime. Diagnosis and treatment at an early stage will typically increase the chances of survival. Tumors can develop therapy resistance, and it is difficult to predict how individual patients will respond to therapy. Most studies that aim to resolve this problem have focused on studying the composition and characteristics of dissociated tumors, while ignoring the role of cell localization and interactions within the tumor microenvironment. In the past decade, technological innovations have enabled multiplex imaging analyses of intact tumors to study localization and interaction parameters, which can be used as biomarkers, or can be correlated with treatment responses and clinical outcomes.The tumor microenvironment is a complex ecosystem containing various cell types, such as immune cells, fibroblasts, and endothelial cells, which interact with the tumor cells. In recent decades, the cancer research field has gained insight into the cellular subtypes that are involved in tumor microenvironment heterogeneity. Moreover, it has become evident that cellular interactions in the tumor microenvironment can either promote or inhibit tumor development, progression, and drug resistance, depending on the context. Multiplex spatial analysis methods have recently been developed; these have offered insight into how cellular crosstalk dynamics and heterogeneity affect cancer prognoses and responses to treatment. Multiplex (imaging) technologies and computational analysis methods allow for the spatial visualization and quantification of cell–cell interactions and properties. These technological advances allow for the discovery of cellular interactions within the tumor microenvironment and provide detailed single-cell information on properties that define cellular behavior. Such analyses give insights into the prognosis and mechanisms of therapy resistance, which is still an urgent problem in the treatment of multiple types of cancer. Here, we provide an overview of multiplex imaging technologies and concepts of downstream analysis methods to investigate cell–cell interactions, how these studies have advanced cancer research, and their potential clinical implications.

Abstract 1686: Characterizing tumor heterogeneity through label-free, morphology-based live cell sorting

Abstract The complexity of cancer is compounded by the continuous emergence of malignant cell populations that drive tumor heterogeneity and challenges diagnostic and therapeutic advances. Methods to study cancer at the single cell level are important to better understand tumor heterogeneity. Isolation of tumor cells from tissue typically relies on targeting biomarkers overexpressed in tumor cells, such as EpCAM, resulting in cells that may express common markers with morphologic and molecular heterogeneity. We developed a platform termed Computational Sorting and Mapping of Single cells (COSMOS) that uses high-dimensional morphology analysis of single cells using artificial intelligence, microfluidics and high resolution bright-field imaging to identify and enrich target cells. We used COSMOS to train a deep convolutional neural network (ConvNet) classifier to identify and enrich malignant cells from non-small cell lung cancer (NSCLC) dissociated tumor cell (DTC) samples. The enriched population of NSCLC cells are label-free, unperturbed and viable, making them amenable to many downstream analysis methods but importantly also contain single cell high-dimensional morphology profiles. We verified enrichment of malignant cells by performing RNA and DNA analysis on the enriched sample. Single cell RNA-seq (scRNA-Seq) analysis shows high levels of EpCAM expression in sorted cells. Copy number variation (CNV) analysis demonstrated increased amplitude of deletion and amplification peaks relative to the pre-sorted DTC sample. Further, mutational analysis show increased allele frequency of mutations including P53 and KRAS in post-sorted compared to pre-sorted samples. Morphological heterogeneity within the malignant population is observed by UMAP analysis with the presence of multiple clusters of morphologically unique tumor cell populations detected. We further trained the classifier to detect and enrich for each subpopulation; CNV, mutation, bulk RNASeq, and scRNA-Seq analysis revealed molecular differences between the morphology subgroups. Further work is planned to evaluate the link between the morphological, molecular and functional characteristics of each subpopulation. We demonstrate a platform that can enrich malignant cells based on morphology, yielding a population of cells that are label-free, viable and unperturbed, making them compatible with downstream molecular assays commonly used to study heterogeneity. Additionally the cells can be morphologically profiled at the single cell level yielding a high-dimensional morphological profile that reveals heterogeneous populations of cells that can be further characterized by molecular analysis and could offer a new dimension to understand heterogeneity and biomarker discovery. Citation Format: Andreja Jovic, Kiran Saini, Michael Phelan, Ryan Chow, Simo Zhang, Chassidy Johnson, Nianzhen Li, Thomas J. Musci, Mahyar Salek, Maddison (Mahdokht) Masaeli. Characterizing tumor heterogeneity through label-free, morphology-based live cell sorting [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1686.

Abstract 2792: On-chip evaluation of cancer cell-extracellular vesicle interactions using a novel microfluidic microsystem (CellExoChip)

Abstract Introduction: Analytical methods of extracellular vesicles (EVs) is becoming an increasingly promising field of study due to them being an effective biomarker for cancers. EVs are present in various types of body fluids, which can be easily used for diagnostic purpose. Prior research has explored numerous techniques for isolating and analyzing these EVs based on their physical and biochemical properties, however, the complexity of biological samples makes conventional EV isolation difficult to exclusively extract EVs of a certain type of cell. Current downstream analysis methods lack the ability to differentiate exosomes of different origins in a sample. Recent studies suggested the presence of certain proteins in cancer exosomes that facilitates preferential uptake of the exosomes by organ-specific cancer cells, called organotropism. Using this unique property, we devised a microfluidic platform to examine the uptake of specific exosomes onto their respective progenitor cell lines, thus aiming to use the interaction for cancer diagnosis purposes. Methods: The CellExoChip was prepared following the standard PDMS-based soft lithography method. The device consists of an inlet and outlet, with dimensions of 22x22 mm with a cell capture area of about 50 mm2. The microfluidic device was functionalized by Streptavidin and the cancer cells were biotinylated with EZ-Link-NHS-Biotin to create an intense affinity between the cells and the device. We further injected dyed exosomes with different origins through the device and evaluated their specific uptake using fluorescence microscope. Results: The prepared CellExoChip successfully immobilized over 1,500 cells onto the surface and viability evaluation demonstrated that only 6.79% of the initial cells were sacrificed during the biotinylation and on-chip binding process. The average on-chip cell viability showed 75.47±7.68%. The uptake of lung cancer cell exosomes into three different cancer cell lines (lung, bladder and breast) was measured on chip. The relative uptake of lung cell exosomes by the respective lung cells was 100% compared to the bladder cells and breast cell which were 15.87% and 40.31%, respectively. We extended this specific uptake evaluation to other LungCell-LungExo combinations using H1650, A549, and in-house CTC cell line, CTCR5, and the results demonstrated the organotropism nature of the exosomes in lung cancer. Discussion and conclusion: We present a novel screening method to accurately characterize specific cancer-derived EVs using a microfluidic platform. This process bypasses the requirement of analyzing and profiling these embedded proteins prior to EV isolation. Our microfluidic device facilitates this interaction between cells and exosomes through the diagnosis process of liquid biopsy. Citation Format: Kruthi Srinivasa Raju, Zeqi Niu, Joseph Marvar, Shawn Fortna, Nna-Emeka Onukwugha, Yoon-Tae Kang, Sunitha Nagrath. On-chip evaluation of cancer cell-extracellular vesicle interactions using a novel microfluidic microsystem (CellExoChip) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2792.

Sample Processing Considerations for Protein Stability Studies of Low Concentration Biofluid Samples using Differential Scanning Calorimetry.

The analysis of biofluid samples with low protein content (e.g., urine or saliva) can be challenging for downstream analysis methods with limited sensitivity. To circumvent this problem, sample processing methods are employed to increase the protein concentration in analyzed samples. However, for some techniques, like differential scanning calorimetry (DSC) that characterizes thermally-induced unfolding of biomolecules, sample processing must not affect native protein structure and stability. We evaluated centrifugal concentration and stirred cell ultrafiltration, two common methods of sample concentration characterized by a low risk of protein denaturation, with the goal of establishing a protocol for DSC analysis of low concentration biospecimens. Our studies indicate that both methods can affect protein stability assessed by DSC and, even after optimization of several parameters, the obtained DSC profile (thermogram) suggested that sample processing affects the structure or intermolecular interactions of component proteins contributing to altered thermal stability detectable by DSC. We also found a relationship between changes in thermograms and low protein concentration, indicating that diluting biospecimens to concentrations below 0.1 mg/mL can perturb the intermolecular environment and affect the structure of proteins present in the solution. Dilution of samples below 0.1 mg/mL, as well as concentration of samples with low protein content, resulted in affected thermogram shapes suggesting changes in protein stability. This should be taken into account when concentrating dilute samples or employing techniques that lower the protein concentration (e.g., fractionation), when downstream applications include techniques, such as DSC, that require the preservation of native protein forms.

An updated view into the cell cycle kinetics of human T lymphocytes and the impact of irradiation

Even though a detailed understanding of the proliferative characteristics of T lymphocytes is imperative in many research fields, prior studies have never reached a consensus on these characteristics, and on the corresponding cell cycle kinetics specifically. In this study, the general proliferative response of human T lymphocytes to phytohaemagglutinin (PHA) stimulation was characterized using a carboxyfluorescein succinimidyl ester-based flow cytometric assay. We were able to determine when PHA-stimulated T lymphocytes complete their first division, the proportion of cells that initiate proliferation, the subsequent division rate of the cells, and the impact of irradiation on these proliferative properties. Next, we accurately visualized the cell cycle progression of dividing T lymphocytes cultured in whole blood using an adapted 5-ethynyl-2’-deoxyuridine pulse-chase method. Furthermore, through multiple downstream analysis methods, we were able to make an estimation of the corresponding cell cycle kinetics. We also visualized the impact of X-rays on the progression of the cells through the cell cycle. Our results showed dose-dependent G2 arrest after exposure to irradiation, and a corresponding delay in G1 phase-entry of the cells. In conclusion, utilizing various flow cytometric assays, we provided valuable information on T lymphocyte proliferation characteristics starting from first division to fully dividing cells.

Current Status of Next-Generation Sequencing Approaches for Candidate Gene Discovery in Familial Parkinson´s Disease.

Parkinson’s disease is a neurodegenerative disorder with a heterogeneous genetic etiology. The advent of next-generation sequencing (NGS) technologies has aided novel gene discovery in several complex diseases, including PD. This Perspective article aimed to explore the use of NGS approaches to identify novel loci in familial PD, and to consider their current relevance. A total of 17 studies, spanning various populations (including Asian, Middle Eastern and European ancestry), were identified. All the studies used whole-exome sequencing (WES), with only one study incorporating both WES and whole-genome sequencing. It is worth noting how additional genetic analyses (including linkage analysis, haplotyping and homozygosity mapping) were incorporated to enhance the efficacy of some studies. Also, the use of consanguineous families and the specific search for de novo mutations appeared to facilitate the finding of causal mutations. Across the studies, similarities and differences in downstream analysis methods and the types of bioinformatic tools used, were observed. Although these studies serve as a practical guide for novel gene discovery in familial PD, these approaches have not significantly resolved the “missing heritability” of PD. We speculate that what is needed is the use of third-generation sequencing technologies to identify complex genomic rearrangements and new sequence variation, missed with existing methods. Additionally, the study of ancestrally diverse populations (in particular those of Black African ancestry), with the concomitant optimization and tailoring of sequencing and analytic workflows to these populations, are critical. Only then, will this pave the way for exciting new discoveries in the field.

Constructing local cell-specific networks from single-cell data

Gene coexpression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level. However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene networks. We develop an approach, locCSN, that estimates cell-specific networks (CSNs) for each cell, preserving information about cellular heterogeneity that is lost with other approaches. LocCSN is based on a nonparametric investigation of the joint distribution of gene expression; hence it can readily detect nonlinear correlations, and it is more robust to distributional challenges. Although individual CSNs are estimated with considerable noise, average CSNs provide stable estimates of networks, which reveal gene communities better than traditional measures. Additionally, we propose downstream analysis methods using CSNs to utilize more fully the information contained within them. Repeated estimates of gene networks facilitate testing for differences in network structure between cell groups. Notably, with this approach, we can identify differential network genes, which typically do not differ in gene expression, but do differ in terms of the coexpression networks. These genes might help explain the etiology of disease. Finally, to further our understanding of autism spectrum disorder, we examine the evolution of gene networks in fetal brain cells and compare the CSNs of cells sampled from case and control subjects to reveal intriguing patterns in gene coexpression.

Evaluation of Missing Value Estimation for Microarray Data

Publisher: School of Statistics, Renmin University of China, Journal: Journal of Data Science, Title: Evaluation of Missing Value Estimation for Microarray Data, Authors: Danh V. Nguyen, Naisyin Wang, Raymond J. Carroll

Acoustofluidic separation enables early diagnosis of traumatic brain injury based on circulating exosomes

Traumatic brain injury (TBI) is a global cause of morbidity and mortality. Initial management and risk stratification of patients with TBI is made difficult by the relative insensitivity of screening radiographic studies as well as by the absence of a widely available, noninvasive diagnostic biomarker. In particular, a blood-based biomarker assay could provide a quick and minimally invasive process to stratify risk and guide early management strategies in patients with mild TBI (mTBI). Analysis of circulating exosomes allows the potential for rapid and specific identification of tissue injury. By applying acoustofluidic exosome separation—which uses a combination of microfluidics and acoustics to separate bioparticles based on differences in size and acoustic properties—we successfully isolated exosomes from plasma samples obtained from mice after TBI. Acoustofluidic isolation eliminated interference from other blood components, making it possible to detect exosomal biomarkers for TBI via flow cytometry. Flow cytometry analysis indicated that exosomal biomarkers for TBI increase in the first 24 h following head trauma, indicating the potential of using circulating exosomes for the rapid diagnosis of TBI. Elevated levels of TBI biomarkers were only detected in the samples separated via acoustofluidics; no changes were observed in the analysis of the raw plasma sample. This finding demonstrated the necessity of sample purification prior to exosomal biomarker analysis. Since acoustofluidic exosome separation can easily be integrated with downstream analysis methods, it shows great potential for improving early diagnosis and treatment decisions associated with TBI.

An Omnibus Test for Differential Distribution Analysis of Continuous Microbiome Data

The human microbiome is comprised of thousands of microbial species. These species will substantially influence the normal physiology of humans and cause numerous diseases. Microbiome data can be measured by sequencing, microarray, or other technologies. With the fast development of these technologies, downstream analysis methods should also be designed to effectively and accurately discover the valuable information that is hidden in the data. Many methods have been designed for the count data of microbiome. However, to our knowledge, there are only a few methods developed for the continuous microbiome data. Many microbiome data have an over-dispersed and zero-inflated data structure. Traditional methods rarely characterize this data structure and only focus on the differences in the abundance between different samples. In this study, we introduce a novel method, the zero-inflated gamma (ZIG) omnibus test, to specifically test the continuous and zero-inflated microbiome data. In this test, abundance will be tested along with zero prevalence and dispersion. We compared this method with five other popular methods. We found that ZIG omnibus test has significantly higher power and a similar or lower false-positive rate than the competing methods in the tests of simulated data. It also found more proved microbiomes in the real data with tonsil cancer. So, we conclude that ZIG omnibus test is a robust method across various biological conditions in the differential expression test of microbiome data.

A framework for assessing 16S rRNA marker-gene survey data analysis methods using mixtures.

BackgroundThere are a variety of bioinformatic pipelines and downstream analysis methods for analyzing 16S rRNA marker-gene surveys. However, appropriate assessment datasets and metrics are needed as there is limited guidance to decide between available analysis methods. Mixtures of environmental samples are useful for assessing analysis methods as one can evaluate methods based on calculated expected values using unmixed sample measurements and the mixture design. Previous studies have used mixtures of environmental samples to assess other sequencing methods such as RNAseq. But no studies have used mixtures of environmental to assess 16S rRNA sequencing.ResultsWe developed a framework for assessing 16S rRNA sequencing analysis methods which utilizes a novel two-sample titration mixture dataset and metrics to evaluate qualitative and quantitative characteristics of count tables. Our qualitative assessment evaluates feature presence/absence exploiting features only present in unmixed samples or titrations by testing if random sampling can account for their observed relative abundance. Our quantitative assessment evaluates feature relative and differential abundance by comparing observed and expected values. We demonstrated the framework by evaluating count tables generated with three commonly used bioinformatic pipelines: (i) DADA2 a sequence inference method, (ii) Mothur a de novo clustering method, and (iii) QIIME an open-reference clustering method. The qualitative assessment results indicated that the majority of Mothur and QIIME features only present in unmixed samples or titrations were accounted for by random sampling alone, but this was not the case for DADA2 features. Combined with count table sparsity (proportion of zero-valued cells in a count table), these results indicate DADA2 has a higher false-negative rate whereas Mothur and QIIME have higher false-positive rates. The quantitative assessment results indicated the observed relative abundance and differential abundance values were consistent with expected values for all three pipelines.ConclusionsWe developed a novel framework for assessing 16S rRNA marker-gene survey methods and demonstrated the framework by evaluating count tables generated with three bioinformatic pipelines. This framework is a valuable community resource for assessing 16S rRNA marker-gene survey bioinformatic methods and will help scientists identify appropriate analysis methods for their marker-gene surveys.

A Protocol for Non-biased Identification of RNAs Transferred Between Heterologous Mammalian Cell Types Using RNA Tagging, Cell Sorting, and Sequencing.

Intercellular communication is a major hallmark of multicellular organisms and is responsible for coordinating cell and tissue differentiation, immune responses, synaptic transmission, and both paracrine and endocrine signaling, for example. Small molecules, peptides, and proteins have all been studied extensively as mediators of intercellular communication; however, RNAs have also been shown recently to transfer between cells. In mammalian cells, microRNAs, tRNAs, short noncoding RNAs, mRNA fragments, as well as full-length mRNAs have all been shown to transfer between cells either by exosomes or by membrane nanotubes. We have previously described nanotube-mediated cell-cell transfer of specific mRNAs between heterologous mammalian cell types cultured in vitro. Here, we describe a simple method for the unbiased and quantitative identification of the complete range of transferred mRNAs (i.e., the mRNA transferome) in one population of mammalian cells following co-culture with another population. After co-culture, the individual cell populations are sorted by magnetic bead-mediated cell sorting and the transferred RNAs are then identified by downstream analysis methods, such as RNA sequencing. Application of this technique not only allows for determination of the mRNA transferome, but can also reveal changes in the native transcriptome of a cell population after co-culture. This can indicate the effect that co-culture and intercellular transfer of mRNA have upon cell physiology.

Unification of miRNA and isomiR research: the mirGFF3 format and the mirtop API.

MicroRNAs (miRNAs) are small RNA molecules (∼22 nucleotide long) involved in post-transcriptional gene regulation. Advances in high-throughput sequencing technologies led to the discovery of isomiRs, which are miRNA sequence variants. While many miRNA-seq analysis tools exist, the diversity of output formats hinders accurate comparisons between tools and precludes data sharing and the development of common downstream analysis methods. To overcome this situation, we present here a community-based project, miRNA Transcriptomic Open Project (miRTOP) working towards the optimization of miRNA analyses. The aim of miRTOP is to promote the development of downstream isomiR analysis tools that are compatible with existing detection and quantification tools. Based on the existing GFF3 format, we first created a new standard format, mirGFF3, for the output of miRNA/isomiR detection and quantification results from small RNA-seq data. Additionally, we developed a command line Python tool, mirtop, to create and manage the mirGFF3 format. Currently, mirtop can convert into mirGFF3 the outputs of commonly used pipelines, such as seqbuster, isomiR-SEA, sRNAbench, Prost! as well as BAM files. Some tools have also incorporated the mirGFF3 format directly into their code, such as, miRge2.0, IsoMIRmap and OptimiR. Its open architecture enables any tool or pipeline to output or convert results into mirGFF3. Collectively, this isomiR categorization system, along with the accompanying mirGFF3 and mirtop API, provide a comprehensive solution for the standardization of miRNA and isomiR annotation, enabling data sharing, reporting, comparative analyses and benchmarking, while promoting the development of common miRNA methods focusing on downstream steps of miRNA detection, annotation and quantification. https://github.com/miRTop/mirGFF3/ and https://github.com/miRTop/mirtop. Supplementary data are available at Bioinformatics online.

Advances in Microfluidics-Based Technologies for Single Cell Culture.

Single cell culture has been considered one of the fundamental tools for single cell studies. Complex biological systems evolve from single cells, and the cells within biological systems are intrinsically heterogeneous. Therefore, culturing and understanding the behaviors of single cells are of great interest for both biological research and clinical studies. In recent years, advances in microfluidics-based technologies have demonstrated unprecedented capabilities for single cell studies, and they have made high-throughput single cell cultures possible. Microfluidic systems enable precise control of the microenvironment for single cell culture and monitoring of the behavior of single cells in real time. In addition, microfluidic devices can consist of upstream cell sorting and cell isolation, and they can also be seamlessly integrated with various downstream analysis methods. Therefore, microfluidic technologies can obtain data about the performance at the single-cell level, providing information that cannot be achieved by studying the ensemble behavior of cell colonies. In this review, the recent developments in droplet-based microfluidics, microwell-based microfluidics, trap-based microfluidics and SlipChip-based microfluidics for the study of single cell culture is focused on. Perspectives on future improvement regarding single cell culture and its related research opportunities are also provided.