RNA profiles reveal signatures of future health and disease in pregnancy.

Maternal morbidity and mortality continue to rise, and pre-eclampsia is a major driver of this burden1. Yet the ability to assess underlying pathophysiology before clinical presentation to enable identification of pregnancies at risk remains elusive. Here we demonstrate the ability of plasma cell-free RNA (cfRNA) to reveal patterns of normal pregnancy progression and determine the risk of developing pre-eclampsia months before clinical presentation. Our results centre on comprehensive transcriptome data from eight independent prospectively collected cohorts comprising 1,840 racially diverse pregnancies and retrospective analysis of 2,539 banked plasma samples. The pre-eclampsia data include 524 samples (72 cases and 452 non-cases) from two diverse independent cohorts collected 14.5 weeks (s.d., 4.5 weeks) before delivery. We show that cfRNA signatures from a single blood draw can track pregnancy progression at the placental, maternal and fetal levels and can robustly predict pre-eclampsia, with a sensitivity of 75% and a positive predictive value of 32.3% (s.d., 3%), which is superior to the state-of-the-art method2. cfRNA signatures of normal pregnancy progression and pre-eclampsia are independent of clinical factors, such as maternal age, body mass index and race, which cumulatively account for less than 1% of model variance. Further, the cfRNA signature for pre-eclampsia contains gene features linked to biological processes implicated in the underlying pathophysiology of pre-eclampsia.

Molecular and genetic regulation of pig pancreatic islet cell development.

Reliance on rodents for understanding pancreatic genetics, development and islet function could limit progress in developing interventions for human diseases such as diabetes mellitus. Similarities of pancreas morphology and function suggest that porcine and human pancreas developmental biology may have useful homologies. However, little is known about pig pancreas development. To fill this knowledge gap, we investigated fetal and neonatal pig pancreas at multiple, crucial developmental stages using modern experimental approaches. Purification of islet ß-, α- and δ-cells followed by transcriptome analysis (RNA-seq) and immunohistology identified cell- and stage-specific regulation, and revealed that pig and human islet cells share characteristic features that are not observed in mice. Morphometric analysis also revealed endocrine cell allocation and architectural similarities between pig and human islets. Our analysis unveiled scores of signaling pathways linked to native islet ß-cell functional maturation, including evidence of fetal α-cell GLP-1 production and signaling to ß-cells. Thus, the findings and resources detailed here show how pig pancreatic islet studies complement other systems for understanding the developmental programs that generate functional islet cells, and that are relevant to human pancreatic diseases.

Predictive RNA profiles for early and very early spontaneous preterm birth.

BACKGROUND: Spontaneous preterm birth remains the main driver of childhood morbidity and mortality. Because of an incomplete understanding of the molecular pathways that result in spontaneous preterm birth, accurate predictive markers and target therapeutics remain elusive. OBJECTIVE: This study sought to determine if a cell-free RNA profile could reveal a molecular signature in maternal blood months before the onset of spontaneous preterm birth. STUDY DESIGN: Maternal samples (n=242) were obtained from a prospective cohort of individuals with a singleton pregnancy across 4 clinical sites at 12-24 weeks (nested case-control; n=46 spontaneous preterm birth <35 weeks and n=194 term controls). Plasma was processed via a next-generation sequencing pipeline for cell-free RNA using the Mirvie RNA platform. Transcripts that were differentially expressed in next-generation sequencing cases and controls were identified. Enriched pathways were identified in the Reactome database using overrepresentation analysis. RESULTS: Twenty five transcripts associated with an increased risk of spontaneous preterm birth were identified. A logistic regression model was developed using these transcripts to predict spontaneous preterm birth with an area under the curve =0.80 (95% confidence interval, 0.72-0.87) (sensitivity=0.76, specificity=0.72). The gene discovery and model were validated through leave-one-out cross-validation. A unique set of 39 genes was identified from cases of very early spontaneous preterm birth (<25 weeks, n=14 cases with time to delivery of 2.5±1.8 weeks); a logistic regression classifier on the basis of these genes yielded an area under the curve=0.76 (95% confidence interval, 0.63-0.87) in leave-one-out cross validation. Pathway analysis for the transcripts associated with spontaneous preterm birth revealed enrichment of genes related to collagen or the extracellular matrix in those who ultimately had a spontaneous preterm birth at <35 weeks. Enrichment for genes in insulin-like growth factor transport and amino acid metabolism pathways were associated with spontaneous preterm birth at <25 weeks. CONCLUSION: Second trimester cell-free RNA profiles in maternal blood provide a noninvasive window to future occurrence of spontaneous preterm birth. The systemic finding of changes in collagen and extracellular matrix pathways may serve to identify individuals at risk for premature cervical remodeling, with growth factor and metabolic pathways implicated more often in very early spontaneous preterm birth. The use of cell-free RNA profiles has the potential to accurately identify those at risk for spontaneous preterm birth by revealing the underlying pathophysiology, creating an opportunity for more targeted therapeutics and effective interventions.

Numerous uncharacterized and highly divergent microbes which colonize humans are revealed by circulating cell-free DNA.

Blood circulates throughout the human body and contains molecules drawn from virtually every tissue, including the microbes and viruses which colonize the body. Through massive shotgun sequencing of circulating cell-free DNA from the blood, we identified hundreds of new bacteria and viruses which represent previously unidentified members of the human microbiome. Analyzing cumulative sequence data from 1,351 blood samples collected from 188 patients enabled us to assemble 7,190 contiguous regions (contigs) larger than 1 kbp, of which 3,761 are novel with little or no sequence homology in any existing databases. The vast majority of these novel contigs possess coding sequences, and we have validated their existence both by finding their presence in independent experiments and by performing direct PCR amplification. When their nearest neighbors are located in the tree of life, many of the organisms represent entirely novel taxa, showing that microbial diversity within the human body is substantially broader than previously appreciated.

Noninvasive Prenatal Diagnosis of Single-Gene Disorders by Use of Droplet Digital PCR.

BACKGROUND: Prenatal diagnosis in pregnancies at risk of single-gene disorders is currently performed using invasive methods such as chorionic villus sampling and amniocentesis. This is in contrast with screening for common aneuploidies, for which noninvasive methods with a single maternal blood sample have become standard clinical practice. METHODS: We developed a protocol for noninvasive prenatal diagnosis of inherited single-gene disorders using droplet digital PCR from circulating cell-free DNA (cfDNA) in maternal plasma. First, the amount of cfDNA and fetal fraction is determined using a panel of TaqMan assays targeting high-variability single-nucleotide polymorphisms. Second, the ratio of healthy and diseased alleles in maternal plasma is quantified using TaqMan assays targeting the mutations carried by the parents. Two validation approaches of the mutation assay are presented. RESULTS: We collected blood samples from 9 pregnancies at risk for different single-gene disorders, including common conditions and rare metabolic disorders. We measured cases at risk of hemophilia, ornithine transcarbamylase deficiency, cystic fibrosis, ß-thalassemia, mevalonate kinase deficiency, acetylcholine receptor deficiency, and DFNB1 nonsyndromic hearing loss. We correctly differentiated affected and unaffected pregnancies (2 affected, 7 unaffected), confirmed by neonatal testing. We successfully measured an affected pregnancy as early as week 11 and with a fetal fraction as low as 3.7% (0.3). CONCLUSIONS: Our method detects single-nucleotide mutations of autosomal recessive diseases as early as the first trimester of pregnancy. This is of importance for metabolic disorders in which early diagnosis can affect management of the disease and reduce complications and anxiety related to invasive testing.

Simultaneously Monitoring Immune Response and Microbial Infections during Pregnancy through Plasma cfRNA Sequencing.

BACKGROUND: Plasma cell-free RNA (cfRNA) encompasses a broad spectrum of RNA species that can be derived from both human cells and microbes. Because cfRNA is fragmented and of low concentration, it has been challenging to profile its transcriptome using standard RNA-seq methods. METHODS: We assessed several recently developed RNA-seq methods on cfRNA samples. We then analyzed the dynamic changes of both the human transcriptome and the microbiome of plasma during pregnancy from 60 women. RESULTS: cfRNA reflects a well-orchestrated immune modulation during pregnancy: an up-regulation of antiinflammatory genes and an increased abundance of antimicrobial genes. We observed that the plasma microbiome remained relatively stable during pregnancy. The bacteria Ureaplasma shows an increased prevalence and increased abundance at postpartum, which is likely to be associated with postpartum infection. We demonstrated that cfRNA-seq can be used to monitor viral infections. We detected a number of human pathogens in our patients, including an undiagnosed patient with a high load of human parvovirus B19 virus (B19V), which is known to be a potential cause of complications in pregnancy. CONCLUSIONS: Plasma cfRNA-seq demonstrates the potential to simultaneously monitor immune response and microbial infections during pregnancy.

Single-molecule kinetics and footprinting of DNA bis-intercalation: the paradigmatic case of Thiocoraline.

DNA bis-intercalators are widely used in molecular biology with applications ranging from DNA imaging to anticancer pharmacology. Two fundamental aspects of these ligands are the lifetime of the bis-intercalated complexes and their sequence selectivity. Here, we perform single-molecule optical tweezers experiments with the peptide Thiocoraline showing, for the first time, that bis-intercalation is driven by a very slow off-rate that steeply decreases with applied force. This feature reveals the existence of a long-lived (minutes) mono-intercalated intermediate that contributes to the extremely long lifetime of the complex (hours). We further exploit this particularly slow kinetics to determine the thermodynamics of binding and persistence length of bis-intercalated DNA for a given fraction of bound ligand, a measurement inaccessible in previous studies of faster intercalating agents. We also develop a novel single-molecule footprinting technique based on DNA unzipping and determine the preferred binding sites of Thiocoraline with one base-pair resolution. This fast and radiolabelling-free footprinting technique provides direct access to the binding sites of small ligands to nucleic acids without the need of cleavage agents. Overall, our results provide new insights into the binding pathway of bis-intercalators and the reported selectivity might be of relevance for this and other anticancer drugs interfering with DNA replication and transcription in carcinogenic cell lines.

Elastic properties and secondary structure formation of single-stranded DNA at monovalent and divalent salt conditions.

Single-stranded DNA (ssDNA) plays a major role in several biological processes. It is therefore of fundamental interest to understand how the elastic response and the formation of secondary structures are modulated by the interplay between base pairing and electrostatic interactions. Here we measure force-extension curves (FECs) of ssDNA molecules in optical tweezers set up over two orders of magnitude of monovalent and divalent salt conditions, and obtain its elastic parameters by fitting the FECs to semiflexible models of polymers. For both monovalent and divalent salts, we find that the electrostatic contribution to the persistence length is proportional to the Debye screening length, varying as the inverse of the square root of cation concentration. The intrinsic persistence length is equal to 0.7 nm for both types of salts, and the effectivity of divalent cations in screening electrostatic interactions appears to be 100-fold as compared with monovalent salt, in line with what has been recently reported for single-stranded RNA. Finally, we propose an analysis of the FECs using a model that accounts for the effective thickness of the filament at low salt condition and a simple phenomenological description that quantifies the formation of non-specific secondary structure at low forces.

Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca2+ imaging and electrophysiology.

I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.

Integration of single-cell multiomic measurements across disease states with genetics identifies mechanisms of beta cell dysfunction in type 2 diabetes.

Altered function and gene regulation of pancreatic islet beta cells is a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of mechanisms driving T2D is still missing. Here we integrate information from measurements of chromatin activity, gene expression and function in single beta cells with genetic association data to identify disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 non-diabetic, pre-T2D and T2D donors, we robustly identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift in T2D. Subtype-defining active chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is likely induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for identifying mechanisms of complex diseases.

Integrating genetics with single-cell multiomic measurements across disease states identifies mechanisms of beta cell dysfunction in type 2 diabetes.

Dysfunctional pancreatic islet beta cells are a hallmark of type 2 diabetes (T2D), but a comprehensive understanding of the underlying mechanisms, including gene dysregulation, is lacking. Here we integrate information from measurements of chromatin accessibility, gene expression and function in single beta cells with genetic association data to nominate disease-causal gene regulatory changes in T2D. Using machine learning on chromatin accessibility data from 34 nondiabetic, pre-T2D and T2D donors, we identify two transcriptionally and functionally distinct beta cell subtypes that undergo an abundance shift during T2D progression. Subtype-defining accessible chromatin is enriched for T2D risk variants, suggesting a causal contribution of subtype identity to T2D. Both beta cell subtypes exhibit activation of a stress-response transcriptional program and functional impairment in T2D, which is probably induced by the T2D-associated metabolic environment. Our findings demonstrate the power of multimodal single-cell measurements combined with machine learning for characterizing mechanisms of complex diseases.

Heterogenous impairment of α cell function in type 2 diabetes is linked to cell maturation state.

In diabetes, glucagon secretion from pancreatic α cells is dysregulated. The underlying mechanisms, and whether dysfunction occurs uniformly among cells, remain unclear. We examined α cells from human donors and mice using electrophysiological, transcriptomic, and computational approaches. Rising glucose suppresses α cell exocytosis by reducing P/Q-type Ca2+ channel activity, and this is disrupted in type 2 diabetes (T2D). Upon high-fat feeding of mice, α cells shift toward a "ß cell-like" electrophysiological profile in concert with indications of impaired identity. In human α cells we identified links between cell membrane properties and cell surface signaling receptors, mitochondrial respiratory chain complex assembly, and cell maturation. Cell-type classification using machine learning of electrophysiology data demonstrated a heterogenous loss of "electrophysiologic identity" in α cells from donors with type 2 diabetes. Indeed, a subset of α cells with impaired exocytosis is defined by an enrichment in progenitor and lineage markers and upregulation of an immature transcriptomic phenotype, suggesting important links between α cell maturation state and dysfunction.

A New Hypothesis for Type 1 Diabetes Risk: The At-Risk Allele at rs3842753 Associates With Increased Beta-Cell INS Messenger RNA in a Meta-Analysis of Single-Cell RNA-Sequencing Data.

OBJECTIVES: Type 1 diabetes is characterized by the autoimmune destruction of insulin-secreting beta cells. Genetic variants upstream at the insulin (INS) locus contribute to ∼10% of type 1 diabetes heritable risk. Previous studies showed an association between rs3842753 C/C genotype and type 1 diabetes susceptibility, but the molecular mechanisms remain unclear. To date, no large-scale studies have looked at the effect of genetic variation at rs3842753 on INS mRNA at the single-cell level. METHODS: We aligned all human islet single-cell RNA sequencing data sets available to us in year 2020 to the reference genome GRCh38.98 and genotyped rs3842753, integrating 2,315 ß cells and 1,223 ß-like cells from 13 A/A protected donors, 23 A/C heterozygous donors and 35 C/C at-risk donors, including adults without diabetes and with type 2 diabetes. RESULTS: INS expression mean and variance were significantly higher in single ß cells from females compared with males. On comparing across ß cells and ß-like cells, we found that rs3842753 C‒containing cells (either homozygous or heterozygous) had the highest INS expression. We also found that ß cells with the rs3842753 C allele had significantly higher endoplasmic reticulum stress marker gene expression compared with the A/A homozygous genotype. CONCLUSIONS: These findings support the emerging concept that inherited risk of type 1 diabetes may be associated with inborn, persistent elevated insulin production, which may lead to ß-cell endoplasmic reticulum stress and fragility.

Combinatorial transcription factor profiles predict mature and functional human islet α and ß cells.

Islet-enriched transcription factors (TFs) exert broad control over cellular processes in pancreatic α and ß cells, and changes in their expression are associated with developmental state and diabetes. However, the implications of heterogeneity in TF expression across islet cell populations are not well understood. To define this TF heterogeneity and its consequences for cellular function, we profiled more than 40,000 cells from normal human islets by single-cell RNA-Seq and stratified α and ß cells based on combinatorial TF expression. Subpopulations of islet cells coexpressing ARX/MAFB (α cells) and MAFA/MAFB (ß cells) exhibited greater expression of key genes related to glucose sensing and hormone secretion relative to subpopulations expressing only one or neither TF. Moreover, all subpopulations were identified in native pancreatic tissue from multiple donors. By Patch-Seq, MAFA/MAFB-coexpressing ß cells showed enhanced electrophysiological activity. Thus, these results indicate that combinatorial TF expression in islet α and ß cells predicts highly functional, mature subpopulations.

Patch-Seq Links Single-Cell Transcriptomes to Human Islet Dysfunction in Diabetes.

Impaired function of pancreatic islet cells is a major cause of metabolic dysregulation and disease in humans. Despite this, it remains challenging to directly link physiological dysfunction in islet cells to precise changes in gene expression. Here we show that single-cell RNA sequencing combined with electrophysiological measurements of exocytosis and channel activity (patch-seq) can be used to link endocrine physiology and transcriptomes at the single-cell level. We collected 1,369 patch-seq cells from the pancreata of 34 human donors with and without diabetes. An analysis of function and gene expression networks identified a gene set associated with functional heterogeneity in ß cells that can be used to predict electrophysiology. We also report transcriptional programs underlying dysfunction in type 2 diabetes and extend this approach to cryopreserved cells from donors with type 1 diabetes, generating a valuable resource for understanding islet cell heterogeneity in health and disease.

Noninvasive blood tests for fetal development predict gestational age and preterm delivery.

Noninvasive blood tests that provide information about fetal development and gestational age could potentially improve prenatal care. Ultrasound, the current gold standard, is not always affordable in low-resource settings and does not predict spontaneous preterm birth, a leading cause of infant death. In a pilot study of 31 healthy pregnant women, we found that measurement of nine cell-free RNA (cfRNA) transcripts in maternal blood predicted gestational age with comparable accuracy to ultrasound but at substantially lower cost. In a related study of 38 women (23 full-term and 15 preterm deliveries), all at elevated risk of delivering preterm, we identified seven cfRNA transcripts that accurately classified women who delivered preterm up to 2 months in advance of labor. These tests hold promise for prenatal care in both the developed and developing worlds, although they require validation in larger, blinded clinical trials.

Experimental measurement of binding energy, selectivity, and allostery using fluctuation theorems.

Thermodynamic bulk measurements of binding reactions rely on the validity of the law of mass action and the assumption of a dilute solution. Yet, important biological systems such as allosteric ligand-receptor binding, macromolecular crowding, or misfolded molecules may not follow these assumptions and may require a particular reaction model. Here we introduce a fluctuation theorem for ligand binding and an experimental approach using single-molecule force spectroscopy to determine binding energies, selectivity, and allostery of nucleic acids and peptides in a model-independent fashion. A similar approach could be used for proteins. This work extends the use of fluctuation theorems beyond unimolecular folding reactions, bridging the thermodynamics of small systems and the basic laws of chemical equilibrium.

Single molecule high-throughput footprinting of small and large DNA ligands.

Most DNA processes are governed by molecular interactions that take place in a sequence-specific manner. Determining the sequence selectivity of DNA ligands is still a challenge, particularly for small drugs where labeling or sequencing methods do not perform well. Here, we present a fast and accurate method based on parallelized single molecule magnetic tweezers to detect the sequence selectivity and characterize the thermodynamics and kinetics of binding in a single assay. Mechanical manipulation of DNA hairpins with an engineered sequence is used to detect ligand binding as blocking events during DNA unzipping, allowing determination of ligand selectivity both for small drugs and large proteins with nearly base-pair resolution in an unbiased fashion. The assay allows investigation of subtle details such as the effect of flanking sequences or binding cooperativity. Unzipping assays on hairpin substrates with an optimized flat free energy landscape containing all binding motifs allows determination of the ligand mechanical footprint, recognition site, and binding orientation.Mapping the sequence specificity of DNA ligands remains a challenge, particularly for small drugs. Here the authors develop a parallelized single molecule magnetic tweezers approach using engineered DNA hairpins that can detect sequence selectivity, thermodynamics and kinetics of binding for small drugs and large proteins.

Elastic Properties of Nucleic Acids by Single-Molecule Force Spectroscopy.

We review the current knowledge on the use of single-molecule force spectroscopy techniques to extrapolate the elastic properties of nucleic acids. We emphasize the lesser-known elastic properties of single-stranded DNA. We discuss the importance of accurately determining the elastic response in pulling experiments, and we review the simplest models used to rationalize the experimental data as well as the experimental approaches used to pull single-stranded DNA. Applications used to investigate DNA conformational transitions and secondary structure formation are also highlighted. Finally, we provide an overview of the effects of salt and temperature and briefly discuss the effects of contour length and sequence dependence.

Electrostatic binding and hydrophobic collapse of peptide-nucleic acid aggregates quantified using force spectroscopy.

Knowledge of the mechanisms of interaction between self-aggregating peptides and nucleic acids or other polyanions is key to the understanding of many aggregation processes underlying several human diseases (e.g., Alzheimer's and Parkinson's diseases). Determining the affinity and kinetic steps of such interactions is challenging due to the competition between hydrophobic self-aggregating forces and electrostatic binding forces. Kahalalide F (KF) is an anticancer hydrophobic peptide that contains a single positive charge that confers strong aggregative properties with polyanions. This makes KF an ideal model to elucidate the mechanisms by which self-aggregation competes with binding to a strongly charged polyelectrolyte such as DNA. We use optical tweezers to apply mechanical forces to single DNA molecules and show that KF and DNA interact in a two-step kinetic process promoted by the electrostatic binding of DNA to the aggregate surface followed by the stabilization of the complex due to hydrophobic interactions. From the measured pulling curves we determine the spectrum of binding affinities, kinetic barriers, and lengths of DNA segments sequestered within the KF-DNA complex. We find there is a capture distance beyond which the complex collapses into compact aggregates stabilized by strong hydrophobic forces and discuss how the bending rigidity of the nucleic acid affects this process. We hypothesize that within an in vivo context, the enhanced electrostatic interaction of KF due to its aggregation might mediate the binding to other polyanions. The proposed methodology should be useful to quantitatively characterize other compounds or proteins in which the formation of aggregates is relevant.