Scalable workflow for characterization of cell-cell communication in COVID-19 patients.

COVID-19 patients display a wide range of disease severity, ranging from asymptomatic to critical symptoms with high mortality risk. Our ability to understand the interaction of SARS-CoV-2 infected cells within the lung, and of protective or dysfunctional immune responses to the virus, is critical to effectively treat these patients. Currently, our understanding of cell-cell interactions across different disease states, and how such interactions may drive pathogenic outcomes, is incomplete. Here, we developed a generalizable and scalable workflow for identifying cells that are differentially interacting across COVID-19 patients with distinct disease outcomes and use this to examine eight public single-cell RNA-seq datasets (six from peripheral blood mononuclear cells, one from bronchoalveolar lavage and one from nasopharyngeal), with a total of 211 individual samples. By characterizing the cell-cell interaction patterns across epithelial and immune cells in lung tissues for patients with varying disease severity, we illustrate diverse communication patterns across individuals, and discover heterogeneous communication patterns among moderate and severe patients. We further illustrate patterns derived from cell-cell interactions are potential signatures for discriminating between moderate and severe patients. Overall, this workflow can be generalized and scaled to combine multiple scRNA-seq datasets to uncover cell-cell interactions.

scClassify: sample size estimation and multiscale classification of cells using single and multiple reference.

Automated cell type identification is a key computational challenge in single-cell RNA-sequencing (scRNA-seq) data. To capitalise on the large collection of well-annotated scRNA-seq datasets, we developed scClassify, a multiscale classification framework based on ensemble learning and cell type hierarchies constructed from single or multiple annotated datasets as references. scClassify enables the estimation of sample size required for accurate classification of cell types in a cell type hierarchy and allows joint classification of cells when multiple references are available. We show that scClassify consistently performs better than other supervised cell type classification methods across 114 pairs of reference and testing data, representing a diverse combination of sizes, technologies and levels of complexity, and further demonstrate the unique components of scClassify through simulations and compendia of experimental datasets. Finally, we demonstrate the scalability of scClassify on large single-cell atlases and highlight a novel application of identifying subpopulations of cells from the Tabula Muris data that were unidentified in the original publication. Together, scClassify represents state-of-the-art methodology in automated cell type identification from scRNA-seq data.

Inferring data-specific micro-RNA function through the joint ranking of micro-RNA and pathways from matched micro-RNA and gene expression data.

MOTIVATION: In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult. RESULTS: We propose a supervised framework, pMim, built upon concepts of significance combination, for jointly ranking regulatory micro-RNA and their potential functional impacts with respect to a condition of interest. Here, pMim directly tests if a micro-RNA is differentially expressed and if its predicted targets, which lie in a common biological pathway, have changed in the opposite direction. We leverage the information within existing micro-RNA target and pathway databases to stabilize the estimation and annotation of micro-RNA regulation making our approach suitable for datasets with small sample sizes. In addition to outputting meaningful and interpretable results, we demonstrate in a variety of datasets that the micro-RNA identified by pMim, in comparison to simpler existing approaches, are also more concordant with what is described in the literature. AVAILABILITY AND IMPLEMENTATION: This framework is implemented as an R function, pMim, in the package sydSeq available from http://www.ellispatrick.com/r-packages. CONTACT: jean.yang@sydney.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of a multimodality blood conservation schema toward improvement of intraoperative hemoglobin levels and off-pump transfusions in coronary artery bypass graft surgery.

BACKGROUND: Cardiothoracic surgery places significant demands on blood bank resources. Measures aimed at reducing intraoperative hemodilution were initiated as part of a blood conservation program. STUDY DESIGN AND METHODS: We initiated a series of measures aimed at reducing hemodilution volume: 1) reduction of intravenous fluid (IVF) volume, 2) reduction of circuit size, and 3) use of autologous priming techniques. All sources and volumes of IVF were obtained from the medical record. Intraoperative hematocrit (Hct) measurements were performed at the following intervals: first in operating room (OR), lowest on-pump, last on-pump, after protamine reversal, and immediately before discharge from OR. Red blood cell (RBC) transfusions were recorded. Intraoperative IVF, Hct levels, and transfusions were analyzed by cardiopulmonary bypass phase (prepump, on-pump, and off-pump), comparing preimplementation and postimplementation periods. RESULTS: Total intraoperative IVF volume was reduced by 973.7 mL (95% confidence interval, 671.6-1275.9 mL; p < 0.001) leading to a mean on-pump Hct improvement of more than 2% (p < 0.004). This contributed to a reduction in off-pump RBC transfusions by 20.6% (p = 0.014). A significant degree of heterogeneity in transfusion practice was noted between anesthesiologists. CONCLUSIONS: Blood conservation efforts in cardiac surgery should include efforts aimed at reducing hemodilution. Potential improvements are blunted by variation in transfusion practice.

Measuring trade-offs that matter: assessing the impact of a new electronic cross-match policy on the turnaround time and the cross-match workload efficiency.

BACKGROUND: Our traditional cross-match (XM) policy generated a significant number of XM units that were never issued. To minimize the unnecessary XM workload, we proposed a new policy where orders eligible for the electronic XM (EXM) are pended until orders to issue red blood cells (RBCs) are received. To address concerns that this new policy might unduly delay blood availability, we conducted a study to assess whether the new policy was noninferior to the traditional policy with regard to the turnaround time (TAT). STUDY DESIGN AND METHODS: We monitored the TAT and XM workload efficiency (XM-to-issue [C : I] ratio) for a total of 8 weeks split between the two policies' periods. The primary outcome was the proportion of RBC issue requests that was turned around in less than 12 minutes. RESULTS: Fifty percent (1133 of 2265) of issue requests were turned around in 12 minutes or less under the traditional policy compared to 43.9% (975 of 2223) under the new policy (absolute difference of 6.1%; 95% confidence interval [CI], 3.2%-9.1%; p < 0.001). The adjusted overall median TAT was slower by 1 minute (13 min vs. 14 min, p < 0.001) but the adjusted C : I ratio was better (1.00 vs. 1.15; p < 0.001) under the new policy. CONCLUSION: Our study showed that the impact of the new policy on the TAT was not inferior to the traditional policy. Since the median TAT of 14 minutes under the new policy met the published benchmarks, the trade-off between delays in the TAT and efficiency gains in the XM workload remained acceptable for patient care.

BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data.

Recent advances in subcellular imaging transcriptomics platforms have enabled high-resolution spatial mapping of gene expression, while also introducing significant analytical challenges in accurately identifying cells and assigning transcripts. Existing methods grapple with cell segmentation, frequently leading to fragmented cells or oversized cells that capture contaminated expression. To this end, we present BIDCell, a self-supervised deep learning-based framework with biologically-informed loss functions that learn relationships between spatially resolved gene expression and cell morphology. BIDCell incorporates cell-type data, including single-cell transcriptomics data from public repositories, with cell morphology information. Using a comprehensive evaluation framework consisting of metrics in five complementary categories for cell segmentation performance, we demonstrate that BIDCell outperforms other state-of-the-art methods according to many metrics across a variety of tissue types and technology platforms. Our findings underscore the potential of BIDCell to significantly enhance single-cell spatial expression analyses, enabling great potential in biological discovery.

Pcdh19 mediates olfactory sensory neuron coalescence during postnatal stages and regeneration.

The mouse olfactory system regenerates constantly throughout life. While genes critical for the initial projection of olfactory sensory neurons (OSNs) to the olfactory bulb have been identified, what genes are important for maintaining the olfactory map during regeneration are still unknown. Here we show a mutation in Protocadherin 19 (Pcdh19), a cell adhesion molecule and member of the cadherin superfamily, leads to defects in OSN coalescence during regeneration. Surprisingly, lateral glomeruli were more affected and males in particular showed a more severe phenotype. Single cell analysis unexpectedly showed OSNs expressing the MOR28 odorant receptor could be subdivided into two major clusters. We showed that at least one protocadherin is differentially expressed between OSNs coalescing on the medial and lateral glomeruli. Moreover, females expressed a slightly different complement of genes from males. These features may explain the differential effects of mutating Pcdh19 on medial and lateral glomeruli in males and females.

Mthfd1 is a modifier of chemically induced intestinal carcinogenesis.

The causal metabolic pathways underlying associations between folate and risk for colorectal cancer (CRC) have yet to be established. Folate-mediated one-carbon metabolism is required for the de novo synthesis of purines, thymidylate and methionine. Methionine is converted to S-adenosylmethionine (AdoMet), the major one-carbon donor for cellular methylation reactions. Impairments in folate metabolism can modify DNA synthesis, genomic stability and gene expression, characteristics associated with tumorigenesis. The Mthfd1 gene product, C1-tetrahydrofolate synthase, is a trifunctional enzyme that generates one-carbon substituted tetrahydrofolate cofactors for one-carbon metabolism. In this study, we use Mthfd1(gt/+) mice, which demonstrate a 50% reduction in C1-tetrahydrofolate synthase, to determine its influence on tumor development in two mouse models of intestinal cancer, crosses between Mthfd1(gt/+) and Apc(min)(/+) mice and azoxymethane (AOM)-induced colon cancer in Mthfd1(gt/+) mice. Mthfd1 hemizygosity did not affect colon tumor incidence, number or load in Apc(min/+) mice. However, Mthfd1 deficiency increased tumor incidence 2.5-fold, tumor number 3.5-fold and tumor load 2-fold in AOM-treated mice. DNA uracil content in the colon was lower in Mthfd1(gt/+) mice, indicating that thymidylate biosynthesis capacity does not play a significant role in AOM-induced colon tumorigenesis. Mthfd1 deficiency-modified cellular methylation potential, as indicated by the AdoMet: S-adenosylhomocysteine ratio and gene expression profiles, suggesting that changes in the transcriptome and/or decreased de novo purine biosynthesis and associated mutability cause cellular transformation in the AOM CRC model. This study emphasizes the impact and complexity of gene-nutrient interactions with respect to the relationships among folate metabolism and colon cancer initiation and progression.

Nanoscale resolution, multicomponent biomolecular arrays generated by aligned printing with parylene peel-off.

We present "Print-and-Peel", a high-throughput method to generate multicomponent biomolecular arrays with sub-100 nm nanoscale feature width. An inkjet printer is first aligned to a parylene template containing nanoscale openings. After printing, the parylene is peeled off to reveal uniformly patterned nanoscale features, despite the imperfect morphologies of the original inkjet spots. We further patterned combinatorial nanoarrays by performing a second print-run superimposed over the first, thereby extending the multiplexing capability of the technique.

Uncovering cell identity through differential stability with Cepo.

The use of single-cell RNA-sequencing (scRNA-seq) allows observation of different cells at multi-tiered complexity in the same microenvironment. To get insights into cell identity using scRNA-seq data, we present Cepo, which generates cell-type-specific gene statistics of differentially stable genes from scRNA-seq data to define cell identity. When applied to multiple datasets, Cepo outperforms current methods in assigning cell identity and enhances several cell identification applications such as cell-type characterisation, spatial mapping of single cells and lineage inference of single cells.

Notch2 is required for maintaining sustentacular cell function in the adult mouse main olfactory epithelium.

Notch receptors are expressed in neurons and glia in the adult nervous system, but why this expression persists is not well-understood. Here we examine the role of the Notch pathway in the postnatal mouse main olfactory system, and show evidence consistent with a model where Notch2 is required for maintaining sustentacular cell function. In the absence of Notch2, the laminar nature of these glial-like cells is disrupted. Hes1, Hey1, and Six1, which are downstream effectors of the Notch pathway, are down-regulated, and cytochrome P450 and Glutathione S-transferase (GST) expression by sustentacular cells is reduced. Functional levels of GST activity are also reduced. These disruptions are associated with increased olfactory sensory neuron degeneration. Surprisingly, expression of Notch3 is also down-regulated. This suggests the existence of a feedback loop where expression of Notch3 is initially independent of Notch2, but requires Notch2 for maintained expression. While the Notch pathway has previously been shown to be important for promoting gliogenesis during development, this is the first demonstration that the persistent expression of Notch receptors is required for maintaining glial function in adult.

Evaluating stably expressed genes in single cells.

BACKGROUND: Single-cell RNA-seq (scRNA-seq) profiling has revealed remarkable variation in transcription, suggesting that expression of many genes at the single-cell level is intrinsically stochastic and noisy. Yet, on the cell population level, a subset of genes traditionally referred to as housekeeping genes (HKGs) are found to be stably expressed in different cell and tissue types. It is therefore critical to question whether stably expressed genes (SEGs) can be identified on the single-cell level, and if so, how can their expression stability be assessed? We have previously proposed a computational framework for ranking expression stability of genes in single cells for scRNA-seq data normalization and integration. In this study, we perform detailed evaluation and characterization of SEGs derived from this framework. RESULTS: Here, we show that gene expression stability indices derived from the early human and mouse development scRNA-seq datasets and the "Mouse Atlas" dataset are reproducible and conserved across species. We demonstrate that SEGs identified from single cells based on their stability indices are considerably more stable than HKGs defined previously from cell populations across diverse biological systems. Our analyses indicate that SEGs are inherently more stable at the single-cell level and their characteristics reminiscent of HKGs, suggesting their potential role in sustaining essential functions in individual cells. CONCLUSIONS: SEGs identified in this study have immediate utility both for understanding variation and stability of single-cell transcriptomes and for practical applications such as scRNA-seq data normalization. Our framework for calculating gene stability index, "scSEGIndex," is incorporated into the scMerge Bioconductor R package (https://sydneybiox.github.io/scMerge/reference/scSEGIndex.html) and can be used for identifying genes with stable expression in scRNA-seq datasets.

Tuning of delta-protocadherin adhesion through combinatorial diversity.

The delta-protocadherins (δ-Pcdhs) play key roles in neural development, and expression studies suggest they are expressed in combination within neurons. The extent of this combinatorial diversity, and how these combinations influence cell adhesion, is poorly understood. We show that individual mouse olfactory sensory neurons express 0-7 δ-Pcdhs. Despite this apparent combinatorial complexity, K562 cell aggregation assays revealed simple principles that mediate tuning of δ-Pcdh adhesion. Cells can vary the number of δ-Pcdhs expressed, the level of surface expression, and which δ-Pcdhs are expressed, as different members possess distinct apparent adhesive affinities. These principles contrast with those identified previously for the clustered protocadherins (cPcdhs), where the particular combination of cPcdhs expressed does not appear to be a critical factor. Despite these differences, we show δ-Pcdhs can modify cPcdh adhesion. Our studies show how intra- and interfamily interactions can greatly amplify the impact of this small subfamily on neuronal function.

Novel subdomains of the mouse olfactory bulb defined by molecular heterogeneity in the nascent external plexiform and glomerular layers.

BACKGROUND: In the mouse olfactory system, the role of the olfactory bulb in guiding olfactory sensory neuron (OSN) axons to their targets is poorly understood. What cell types within the bulb are necessary for targeting is unknown. What genes are important for this process is also unknown. Although projection neurons are not required, other cell-types within the external plexiform and glomerular layers also form synapses with OSNs. We hypothesized that these cells are important for targeting, and express spatially differentially expressed guidance cues that act to guide OSN axons within the bulb. RESULTS: We used laser microdissection and microarray analysis to find genes that are differentially expressed along the dorsal-ventral, medial-lateral, and anterior-posterior axes of the bulb. The expression patterns of these genes divide the bulb into previously unrecognized subdomains. Interestingly, some genes are expressed in both the medial and lateral bulb, showing for the first time the existence of symmetric expression along this axis. We use a regeneration paradigm to show that several of these genes are altered in expression in response to deafferentation, consistent with the interpretation that they are expressed in cells that interact with OSNs. CONCLUSION: We demonstrate that the nascent external plexiform and glomerular layers of the bulb can be divided into multiple domains based on the expression of these genes, several of which are known to function in axon guidance, synaptogenesis, and angiogenesis. These genes represent candidate guidance cues that may act to guide OSN axons within the bulb during targeting.

Analysis of the gene regulatory program induced by the homeobox transcription factor distal-less 3 in mouse placenta.

Dlx3, a homeodomain transcription factor, is essential for placental development in the mouse. The Dlx3(-/-) mouse embryo dies at embryonic d 9.5-10 putatively due to placental failure. To develop a more comprehensive understanding of the gene profile regulated by Dlx3, microarray analysis was used to determine differences in gene expression within the placenta of Dlx3(+/+) and Dlx3(-/-) mice. Array analysis revealed differential expression of 401 genes, 33 genes in which signal to log ratio values of null/wild-type were lower than -0.5 or higher than 0.5. To corroborate these findings, quantitative real-time PCR was used to confirm differential expression for 11 genes, nine of which displayed reduced expression and two with enhanced expression in the Dlx3(-/-) mouse. Loss of Dlx3 resulted in a marked reduction (>60%) in mRNA expression of placental growth factor (Pgf), a member of the vascular endothelial growth factor family. Consistent with these results, Pgf secretion from placental explants tended to be reduced in the Dlx3(-/-) mice, compared with wild type. To investigate mechanisms of Dlx3 regulation of Pgf gene transcription, we cloned 5.2 kb of the Pgf 5' flanking sequence for use in reporter gene assays. Expression of the Pgf promoter luciferase reporter containing at least three Dlx3 binding sites was increased markedly by overexpression of Dlx3 supporting the conclusion that Dlx3 may have a direct effect on Pgf promoter activity. These studies provide a novel view of the transcriptome regulated by Dlx3 in mouse placenta. Dlx3 is specifically required for full expression and secretion of Pgf in vivo. Moreover, in vitro studies support the conclusion that Dlx3 is sufficient to directly modulate expression of the Pgf gene promoter in placental cells.

Characterization of mRNA expression in single neurons.

How neurons differ from each other is largely determined by their specific repertoire of mRNAs. The genes expressed by a given neuron reflect its developmental history, its interaction with other cells, and its synaptic activity. Since the introduction of reverse transcription polymerase chain reaction (RT-PCR), it has been possible to identify specific mRNAs present in small samples of total RNA. But isolating RNA from only those cells of interest, and not others, represents a significant challenge. Several approaches can be used to isolate RNA from selected neurons. Following whole-cell patch-clamp recording, mRNA can be harvested from living cells by aspirating the cytoplasm into the patch-clamp pipette. Transcripts expressed in the recorded neuron can then be amplified by RT-PCR. Another way of isolating identified neurons is to use cell-specific promoters to drive the expression of a marker gene such as green fluorescent protein (GFP). RNA can then be isolated from GFP-positive cells. In a tissue context, laser microdissection can also be used to excise the cells of interest directly into an RNA isolation solution. The above methods of RNA isolation can also be combined with RNA amplification and microarray technology to identify specific transcripts that are unique to the cell type being studied. Here we provide detailed protocols for harvesting RNA from single cells, methods for RNA purification, and PCR amplification.

A Subset of Dogs with Presumptive Idiopathic Epilepsy Show Hippocampal Asymmetry: A Volumetric Comparison with Non-Epileptic Dogs Using MRI.

MRI-acquired volumetric measurements from 100 dogs with presumptive idiopathic epilepsy (IE) and 41 non-epileptic (non-IE) dogs were used to determine if hippocampal asymmetry exists in the IE as compared to the non-IE dogs. MRI databases from three institutions were searched for dogs that underwent MRI of the brain and were determined to have IE and those that were considered non-IE dogs. Volumes of the right and left hippocampi were measured using Mimics® software. Median hippocampal volumes of IE and non-IE dogs were 0.47 and 0.53 cm3, respectively. There was no significant difference in overall hippocampal volume between IE and non-IE dogs; however, IE dogs had greater hippocampal asymmetry than non-IE dogs (P < 0.012). A threshold value of 1.16 from the hippocampal ratio had an 85% specificity for identifying IE-associated asymmetry. Thirty five percent of IE dogs had a hippocampal ratio >1.16. Asymmetry was not associated with any particular hemisphere (P = 0.67). Our study indicates that hippocampal asymmetry occurs in a subset of dogs with presumptive idiopathic/genetic epilepsy, suggesting a structural etiology to some cases of IE.

EST2Prot: mapping EST sequences to proteins.

BACKGROUND: EST libraries are used in various biological studies, from microarray experiments to proteomic and genetic screens. These libraries usually contain many uncharacterized ESTs that are typically ignored since they cannot be mapped to known genes. Consequently, new discoveries are possibly overlooked. RESULTS: We describe a system (EST2Prot) that uses multiple elements to map EST sequences to their corresponding protein products. EST2Prot uses UniGene clusters, substring analysis, information about protein coding regions in existing DNA sequences and protein database searches to detect protein products related to a query EST sequence. Gene Ontology terms, Swiss-Prot keywords, and protein similarity data are used to map the ESTs to functional descriptors. CONCLUSION: EST2Prot extends and significantly enriches the popular UniGene mapping by utilizing multiple relations between known biological entities. It produces a mapping between ESTs and proteins in real-time through a simple web-interface. The system is part of the Biozon database and is accessible at http://biozon.org/tools/est/.

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.

Integrated single cell data analysis reveals cell specific networks and novel coactivation markers.

BACKGROUND: Large scale single cell transcriptome profiling has exploded in recent years and has enabled unprecedented insight into the behavior of individual cells. Identifying genes with high levels of expression using data from single cell RNA sequencing can be useful to characterize very active genes and cells in which this occurs. In particular single cell RNA-Seq allows for cell-specific characterization of high gene expression, as well as gene coexpression. RESULTS: We offer a versatile modeling framework to identify transcriptional states as well as structures of coactivation for different neuronal cell types across multiple datasets. We employed a gamma-normal mixture model to identify active gene expression across cells, and used these to characterize markers for olfactory sensory neuron cell maturity, and to build cell-specific coactivation networks. We found that combined analysis of multiple datasets results in more known maturity markers being identified, as well as pointing towards some novel genes that may be involved in neuronal maturation. We also observed that the cell-specific coactivation networks of mature neurons tended to have a higher centralization network measure than immature neurons. CONCLUSION: Integration of multiple datasets promises to bring about more statistical power to identify genes and patterns of interest. We found that transforming the data into active and inactive gene states allowed for more direct comparison of datasets, leading to identification of maturity marker genes and cell-specific network observations, taking into account the unique characteristics of single cell transcriptomics data.