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Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues.

ABSTRACT: Existing genomic sequencing data can be used to study host-microbiome ecosystems, however distinguishing signals originating from truly present microbes versus contaminating species and artifacts is a substantial and often prohibitive challenge. Here we show that emerging sequencing technologies definitely capture reads from present microbes. We developed SAHMI, a computational resource to identify truly present microbial nucleic acids and filter contaminants and spurious false-positive taxonomic assignments from standard transcriptomic sequencing of mammalian tissues. In benchmark studies, SAHMI correctly identifies known microbial infections present in diverse tissues, and we validate SAHMI's enrichment for correctly classified, truly present species using multiple orthogonal computational experiments. The application of SAHMI to single-cell and spatial genomic data thus enables co-detection of somatic cells and microorganisms and joint analysis of host-microbiome ecosystems.

SUBMITTER: Ghaddar B

PROVIDER: S-EPMC10634611 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues.

Ghaddar Bassel B Blaser Martin J MJ De Subhajyoti S

Nature computational science 20230918 9

Existing genomic sequencing data can be used to study host-microbiome ecosystems, however distinguishing signals originating from truly present microbes versus contaminating species and artifacts is a substantial and often prohibitive challenge. Here we show that emerging sequencing technologies definitely capture reads from present microbes. We developed SAHMI, a computational resource to identify truly present microbial nucleic acids and filter contaminants and spurious false-positive taxonomi ...[more]

PMID: 37946872

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Project description:BackgroundIn human microbiome studies, it is crucial to evaluate the association between microbial group (e.g., community or clade) composition and a host phenotype of interest. In response, a number of microbial group association tests have been proposed, which account for the unique features of the microbiome data (e.g., high-dimensionality, compositionality, phylogenetic relationship). These tests generally fall in the class of aggregation tests which amplify the overall group association by combining all the underlying microbial association signals, and, therefore, they are powerful when many microbial species are associated with a given host phenotype (i.e., low sparsity). However, in practice, the microbial association signals can be highly sparse, and this is especially the situation where we have a difficulty to discover the microbial group association.MethodsHere, we introduce a powerful microbial group association test for sparse microbial association signals, namely, microbiome higher criticism analysis (MiHC). MiHC is a data-driven omnibus test taken in a search space spanned by tailoring the higher criticism test to incorporate phylogenetic information and/or modulate sparsity levels and including the Simes test for excessively high sparsity levels. Therefore, MiHC robustly adapts to diverse phylogenetic relevance and sparsity levels.ResultsOur simulations show that MiHC maintains a high power at different phylogenetic relevance and sparsity levels with correct type I error controls. We also apply MiHC to four real microbiome datasets to test the association between respiratory tract microbiome and smoking status, the association between the infant's gut microbiome and delivery mode, the association between the gut microbiome and type 1 diabetes status, and the association between the gut microbiome and human immunodeficiency virus status.ConclusionsIn practice, the true underlying association pattern on the extent of phylogenetic relevance and sparsity is usually unknown. Therefore, MiHC can be a useful analytic tool because of its high adaptivity to diverse phylogenetic relevance and sparsity levels. MiHC can be implemented in the R computing environment using our software package freely available at https://github.com/hk1785/MiHC.

Project description:BackgroundThe single-cell platform provided revolutionary way to study cellular biology. Technologically, a sophistic protocol of isolating qualified single cells would be key to deliver to single-cell platform, which requires high cell viability, high cell yield and low content of cell aggregates or doublets. For musculoskeletal tissues, like bone, cartilage, nucleus pulposus and tendons, as well as their pathological state, which are tense and dense, it's full of challenge to efficiently and rapidly prepare qualified single-cell suspension. Conventionally, enzymatic dissociation methods were wildly used but lack of quality control. In the present study, we designed the rapid cycling enzymatic processing method using tissue-specific enzyme cocktail to treat different human pathological musculoskeletal tissues, including degenerated nucleus pulposus (NP), ossifying posterior longitudinal ligament (OPLL) and knee articular cartilage (AC) with osteoarthritis aiming to rapidly and efficiently harvest qualified single-cell suspensions for single-cell RNA-sequencing (scRNA-seq).ResultsWe harvested highly qualified single-cell suspensions from NP and OPLL with sufficient cell numbers and high cell viability using the rapid cycling enzymatic processing method, which significantly increased the cell viability compared with the conventional long-time continuous digestion group (P < 0.05). Bioanalyzer trace showed expected cDNA size distribution of the scRNA-seq library and a clear separation of cellular barcodes from background partitions were verified by the barcode-rank plot after sequencing. T-SNE visualization revealed highly heterogeneous cell subsets in NP and OPLL. Unfortunately, we failed to obtain eligible samples from articular cartilage due to low cell viability and excessive cell aggregates and doublets.ConclusionsIn conclusion, using the rapid cycling enzymatic processing method, we provided thorough protocols for preparing single-cell suspensions from human musculoskeletal tissues, which was timesaving, efficient and protective to cell viability. The strategy would greatly guarantee the cell heterogeneity, which is critical for scRNA-seq data analysis. The protocol to treat human OA articular cartilage should be further improved.

Dataset Information

Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues.

Publications

Denoising sparse microbial signals from single-cell sequencing of mammalian host tissues.

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