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Metagenomic DNA sequencing is a powerful tool to characterize microbial communities but is sensitive to environmental DNA contamination, in particular when applied to samples with low microbial biomass. Here, we present Sample-Intrinsic microbial DNA Found by Tagging and sequencing (SIFT-seq) a metagenomic sequencing assay that is robust against environmental DNA contamination introduced during sample preparation. The core idea of SIFT-seq is to tag the DNA in the sample prior to DNA isolation and library preparation with a label that can be recorded by DNA sequencing. Any contaminating DNA that is introduced in the sample after tagging can then be bioinformatically identified and removed. We applied SIFT-seq to screen for infections from microorganisms with low burden in blood and urine, to identify COVID-19 co-infection, to characterize the urinary microbiome, and to identify microbial DNA signatures of sepsis and inflammatory bowel disease in blood. The accuracy of metagenomic DNA sequencing is limited by environmental DNA contamination. Here, the authors develop and test SIFT-seq, a metagenomic DNA sequencing assay that allows to identify and remove environmental DNA contamination introduced during sample preparation.

Urine is a rich source of nucleic acid biomarkers including cell-free DNA (cfDNA) and RNA for monitoring the health of kidney allografts. In this study, we aimed to evaluate whether urine filtration can serve as an alternative to the commonly used method of centrifugation to collect urinary fluid and cell pellets for isolating cfDNA and cellular messenger RNA (mRNA). We collected urine specimens from kidney allograft recipients and obtained the urine supernatant and cell pellet from each specimen using both filtration and centrifugation for paired analyses. We performed DNA sequencing to characterize the origin and properties of cfDNA, as well as quantitative PCR of mRNAs extracted from cell fractions. Our results showed that the biophysical properties of cfDNA, the microbial DNA content, and the tissues of origin of cfDNA were comparable between samples processed using filtration and centrifugation method. Similarly, mRNA quality and quantity obtained using both methods met our criteria for downstream application and the Ct values for each mRNA were comparable between the two techniques.The Ct values demonstrated a high degree of correlation. These findings suggest that urine filtration is a viable alternative to urine centrifugation for isolation of nucleic acid biomarkers from urine specimens.

Circulating tumor DNA (ctDNA) is a promising diagnostic marker for many cancers and can be noninvasively assayed from blood. For diagnosing glioma, this approach has unfortunately proven to be of limited use since glioma contribute minimal ctDNA to the blood circulation. A more promising avenue may therefore be to hunt for ctDNA in cerebrospinal fluid (CSF). The study by Mouliere et al in this issue of EMBO Molecular Medicine demonstrates that shallow whole‐genome sequencing of CSF‐cfDNA can be used to detect copy number alterations in glioma‐derived ctDNA, providing a low cost strategy to screen for glioma. Graphical Abstract De Vlaminck & colleagues discuss the low‐cost screening strategy developed by Mouliere et al (in this issue of EMBO Molecular Medicine ) that takes advantage of shallow whole‐genome sequencing of tumor‐derived cell‐free DNA in the cerebrospinal fluid to identify gliomas.

Background Cell-free DNA (cfDNA) in blood, urine, and other biofluids provides a unique window into human health. A proportion of cfDNA is derived from bacteria and viruses, creating opportunities for the diagnosis of infection via metagenomic sequencing. The total biomass of microbial-derived cfDNA in clinical isolates is low, which makes metagenomic cfDNA sequencing susceptible to contamination and alignment noise. Results Here, we report low biomass background correction (LBBC), a bioinformatics noise filtering tool informed by the uniformity of the coverage of microbial genomes and the batch variation in the absolute abundance of microbial cfDNA. We demonstrate that LBBC leads to a dramatic reduction in false positive rate while minimally affecting the true positive rate for a cfDNA test to screen for urinary tract infection. We next performed high-throughput sequencing of cfDNA in amniotic fluid collected from term uncomplicated pregnancies or those complicated with clinical chorioamnionitis with and without intra-amniotic infection. Conclusions The data provide unique insight into the properties of fetal and maternal cfDNA in amniotic fluid, demonstrate the utility of cfDNA to screen for intra-amniotic infection, support the view that the amniotic fluid is sterile during normal pregnancy, and reveal cases of intra-amniotic inflammation without infection at term. 7ESvei6brGwyYZ7yuuRbtx Video abstract.

High-throughput metagenomic sequencing offers an unbiased approach to identify pathogens in clinical samples. Conventional metagenomic sequencing, however, does not integrate information about the host, which is often critical to distinguish infection from infectious disease, and to assess the severity of disease. Here, we explore the utility of high-throughput sequencing of cell-free DNA (cfDNA) after bisulfite conversion to map the tissue and cell types of origin of host-derived cfDNA, and to profile the bacterial and viral metagenome. We applied this assay to 51 urinary cfDNA isolates collected from a cohort of kidney transplant recipients with and without bacterial and viral infection of the urinary tract. We find that the cell and tissue types of origin of urinary cfDNA can be derived from its genome-wide profile of methylation marks, and strongly depend on infection status. We find evidence of kidney and bladder tissue damage due to viral and bacterial infection, respectively, and of the recruitment of neutrophils to the urinary tract during infection. Through direct comparison to conventional metagenomic sequencing as well as clinical tests of infection, we find this assay accurately captures the bacterial and viral composition of the sample. The assay presented here is straightforward to implement, offers a systems view into bacterial and viral infections of the urinary tract, and can find future use as a tool for the differential diagnosis of infection.

Allogeneic hematopoietic cell transplantation (HCT) provides effective treatment for hematologic malignancies and immune disorders. Monitoring of posttransplant complications is critical, yet current diagnostic options are limited. Here, we show that cell-free DNA (cfDNA) in blood is a versatile analyte for monitoring of the most important complications that occur after HCT: graft-versus-host disease (GVHD), a frequent immune complication of HCT, infection, relapse of underlying disease, and graft failure. We demonstrate that these therapeutic complications are informed from a single assay, low-coverage bisulfite sequencing of cfDNA, followed by disease-specific bioinformatic analyses. To inform GVHD, we profile cfDNA methylation marks to trace the cfDNA tissues-of-origin and to quantify tissue-specific injury. To inform infection, we implement metagenomic cfDNA profiling. To inform cancer relapse, we implement analyses of tumor-specific genomic aberrations. Finally, to detect graft failure, we quantify the proportion of donor- and recipient-specific cfDNA. We applied this assay to 170 plasma samples collected from 27 HCT recipients at predetermined timepoints before and after allogeneic HCT. We found that the abundance of solid-organ–derived cfDNA in the blood at 1 mo after HCT is predictive of acute GVHD (area under the curve, 0.88). Metagenomic profiling of cfDNA revealed the frequent occurrence of viral reactivation in this patient population. The fraction of donor-specific cfDNA was indicative of relapse and remission, and the fraction of tumor-specific cfDNA was informative of cancer relapse. This proof-of-principle study shows that cfDNA has the potential to improve the care of allogeneic HCT recipients by enabling earlier detection and better prediction of the complex array of complications that occur after HCT.

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGE SNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by ~300× compared to previous WGS error suppression. MRD-EDGE CNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, vastly expanding its applicability within solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in TF in response to neoadjuvant immunotherapy in lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGE SNV enables plasma-only (non-tumor-informed) disease monitoring in advanced melanoma and lung cancer, yielding clinically informative TF monitoring for patients on immune-checkpoint inhibition. Detection of circulating tumor DNA using MRD-EDGE, a machine-learning-guided single-nucleotide variant and copy-number variant detection platform for signal enrichment, enables monitoring of minimal residual disease and immunotherapy response in settings of low tumor burden.

The human somatic genome evolves throughout our lifespan, producing mosaic individuals comprising clones harboring different mutations across tissues. While clonal expansions in the hematopoietic system have been extensively characterized and reported to be nearly ubiquitous, clonal mosaicism (CM) has more recently also been described across multiple solid tissues. However, outstanding questions remain about the parameters and processes of human somatic evolution in non-cancerous solid human tissues, including when clones arise, how they evolve over time, and what mechanisms lead to their expansion. Questions of timing and clonal dynamics can be addressed through phylogenetic reconstruction, which serves as a 'temporal microscope', while uncovering the mechanisms of expansion necessitates simultaneous phenotypic profiling. To address this gap, here we develop Single-cell Miniaturized Automated Reverse Transcription and Primary Template-directed Amplification (SMART-PTA) for joint single-cell whole-genome and whole-transcriptome sequencing for large scale and cost efficient interrogation of solid tissue CM. We established a workflow that generates hundreds of matched single-cell whole genome and transcriptome libraries within a week. We profiled phenotypically normal esophagus tissue from four aged donors and used somatic variants to build high-resolution single-cell lineages from >2,700 cells with accompanying transcriptomic information, reconstructing >70 years of somatic evolution. T cell expansions identified from T cell receptor (TCR) sequences validated the clonal structure of the single-nucleotide variant (SNV)-based phylogenies and phylogenetic cross-correlation analysis showed that epithelial cells had higher degrees of shared ancestry by spatial location compared to immune cells. Mapping mutation signatures to the phylogenetic tree revealed the emergence of tobacco/alcohol exposure-related signatures later in life, consistent with the donors' exposure histories. We identified variants in driver genes that were previously reported in the phenotypically normal esophagus, detecting clonal expansions harboring mutations in genes including and . We mapped the evolution of clones with both monoallelic as well as biallelic loss, including a clone associated with high expression of cell cycling genes and higher chromosome instability. Leveraging the matched transcriptome data, we uncovered cell type biases in mutant clones, with a higher protphortion of or -mutant cells in an earlier basal epithelial cell state compared to wild-type cells. We further observed copy-neutral loss of heterozygosity (CNLOH) events on chromosome 9q that spanned the locus in up to ~35% of epithelial cells. Mapping CNLOH events to the phylogenetic tree revealed a striking pattern in which CNLOH was separately acquired many times, reflecting convergent evolution. Cells with CNLOH events were biased towards the earlier basal epithelial state, suggestive of a selective advantage that leads to prevalent recurrence of chr9q CNLOH. Together, we demonstrate that SMART-PTA is an efficient, scalable approach for single-cell whole-genome and whole-transcriptome profiling to build phenotypically annotated single-cell phylogenies with enough throughput and power for application to normal tissue somatic evolution. Moreover, we reconstruct the evolutionary history of the esophageal epithelium at high scale and resolution, providing a window into the dynamics and processes that shape clonal expansions in phenotypically normal tissues throughout a lifespan.

Distinguishing real biological variation in the form of single-nucleotide variants (SNVs) from errors is a major challenge for genome sequencing technologies. This is particularly true in settings where SNVs are at low frequency such as cancer detection through liquid biopsy, or human somatic mosaicism. State-of-the-art molecular denoising approaches for DNA sequencing rely on duplex sequencing, where both strands of a single DNA molecule are sequenced to discern true variants from errors arising from single stranded DNA damage. However, such duplex approaches typically require massive over-sequencing to overcome low capture rates of duplex molecules. To address these challenges, we introduce paired plus-minus sequencing (ppmSeq) technology, in which both DNA strands are partitioned and clonally amplified on sequencing beads through emulsion PCR. In this reaction, both strands of a double-stranded DNA molecule contribute to a single sequencing read, allowing for a duplex yield that scales linearly with sequencing coverage across a wide range of inputs (1.8-98 ng). We benchmarked ppmSeq against current duplex sequencing technologies, demonstrating superior duplex recovery with ppmSeq, with a rate of 44%±5.5% (compared to ~5-11% for leading duplex technologies). Using both genomic as well as cell-free DNA, we established error rates for ppmSeq, which had residual SNV detection error rates as low as 7.98x10 for gDNA (using an end-repair protocol with dideoxy nucleotides) and 3.5x10 ±7.5x10 for cell-free DNA. To test the capabilities of ppmSeq for error-corrected whole-genome sequencing (WGS) for clinical application, we assessed circulating tumor DNA (ctDNA) detection for disease monitoring in cancer patients. We demonstrated that ppmSeq enables powerful tumor-informed ctDNA detection at concentrations of 10 across most cancers, and up to 10 in cancers with high mutation burden. We then leveraged genome-wide trinucleotide mutation patterns characteristic of urothelial (APOBEC3-related and platinum exposure-related signatures) and lung (tobacco-exposure-related signatures) cancers to perform tumor-naive ctDNA detection, showing that ppmSeq can identify a disease-specific signal in plasma cell-free DNA without a matched tumor, and that this signal correlates with imaging-based disease metrics. Altogether, ppmSeq provides an error-corrected, cost-efficient and scalable approach for high-fidelity WGS that can be harnessed for challenging clinical applications and emerging frontiers in human somatic genetics where high accuracy is required for mutation identification.

Metagenomic DNA sequencing is a powerful tool to characterize microbial communities but is sensitive to environmental DNA contamination, in particular when applied to samples with low microbial biomass. Here, we present contamination-free metagenomic DNA sequencing (Coffee-seq), a metagenomic sequencing assay that is robust against environmental contamination. The core idea of Coffee-seq is to tag the DNA in the sample prior to DNA isolation and library preparation with a label that can be recorded by DNA sequencing. Any contaminating DNA that is introduced in the sample after tagging can then be bioinformatically identified and removed. We applied Coffee-seq to screen for infections from microorganisms with low burden in blood and urine, to identify COVID-19 co-infection, to characterize the urinary microbiome, and to identify microbial DNA signatures of inflammatory bowel disease in blood.