Mapping transcriptomic vector fields of single cells.

Single-cell (sc)RNA-seq, together with RNA velocity and metabolic labeling, reveals cellular states and transitions at unprecedented resolution. Fully exploiting these data, however, requires kinetic models capable of unveiling governing regulatory functions. Here, we introduce an analytical framework dynamo (https://github.com/aristoteleo/dynamo-release), which infers absolute RNA velocity, reconstructs continuous vector fields that predict cell fates, employs differential geometry to extract underlying regulations, and ultimately predicts optimal reprogramming paths and perturbation outcomes. We highlight dynamo's power to overcome fundamental limitations of conventional splicing-based RNA velocity analyses to enable accurate velocity estimations on a metabolically labeled human hematopoiesis scRNA-seq dataset. Furthermore, differential geometry analyses reveal mechanisms driving early megakaryocyte appearance and elucidate asymmetrical regulation within the PU.1-GATA1 circuit. Leveraging the least-action-path method, dynamo accurately predicts drivers of numerous hematopoietic transitions. Finally, in silico perturbations predict cell-fate diversions induced by gene perturbations. Dynamo, thus, represents an important step in advancing quantitative and predictive theories of cell-state transitions.

Maturation Kinetics of a Multiprotein Complex Revealed by Metabolic Labeling.

All proteins interact with other cellular components to fulfill their function. While tremendous progress has been made in the identification of protein complexes, their assembly and dynamics remain difficult to characterize. Here, we present a high-throughput strategy to analyze the native assembly kinetics of protein complexes. We apply our approach to characterize the co-assembly for 320 pairs of nucleoporins (NUPs) constituting the ≈50 MDa nuclear pore complex (NPC) in yeast. Some NUPs co-assemble fast via rapid exchange whereas others require lengthy maturation steps. This reveals a hierarchical principle of NPC biogenesis where individual subcomplexes form on a minute timescale and then co-assemble from center to periphery in a ∼1 h-long maturation process. Intriguingly, the NUP Mlp1 stands out as joining very late and associating preferentially with aged NPCs. Our approach is readily applicable beyond the NPC, making it possible to analyze the intracellular dynamics of a variety of multiprotein assemblies.

Widespread Influence of 3'-End Structures on Mammalian mRNA Processing and Stability.

The physiological relevance of structures within mammalian mRNAs has been elusive, as these mRNAs are less folded in cells than in vitro and have predicted secondary structures no more stable than those of random sequences. Here, we investigate the possibility that mRNA structures facilitate the 3'-end processing of thousands of human mRNAs by juxtaposing poly(A) signals (PASs) and cleavage sites that are otherwise too far apart. We find that RNA structures are predicted to be more prevalent within these extended 3'-end regions than within PAS-upstream regions and indeed are substantially more folded within cells, as determined by intracellular probing. Analyses of thousands of ectopically expressed variants demonstrate that this folding both enhances processing and increases mRNA metabolic stability. Even folds with predicted stabilities resembling those of random sequences can enhance processing. Structure-controlled processing can also regulate neighboring gene expression. Thus, RNA structure has widespread roles in mammalian mRNA biogenesis and metabolism.

Kinetic Analysis of Protein Stability Reveals Age-Dependent Degradation.

Do young and old protein molecules have the same probability to be degraded? We addressed this question using metabolic pulse-chase labeling and quantitative mass spectrometry to obtain degradation profiles for thousands of proteins. We find that >10% of proteins are degraded non-exponentially. Specifically, proteins are less stable in the first few hours of their life and stabilize with age. Degradation profiles are conserved and similar in two cell types. Many non-exponentially degraded (NED) proteins are subunits of complexes that are produced in super-stoichiometric amounts relative to their exponentially degraded (ED) counterparts. Within complexes, NED proteins have larger interaction interfaces and assemble earlier than ED subunits. Amplifying genes encoding NED proteins increases their initial degradation. Consistently, decay profiles can predict protein level attenuation in aneuploid cells. Together, our data show that non-exponential degradation is common, conserved, and has important consequences for complex formation and regulation of protein abundance.

MicroRNAs Cause Accelerated Decay of Short-Tailed Target mRNAs.

MicroRNAs (miRNAs) specify the recruitment of deadenylases to mRNA targets. Despite this recruitment, we find that miRNAs have almost no effect on steady-state poly(A)-tail lengths of their targets in mouse fibroblasts, which motivates the acquisition of pre-steady-state measurements of the effects of miRNAs on tail lengths, mRNA levels, and translational efficiencies. Effects on translational efficiency are minimal compared to effects on mRNA levels, even for newly transcribed target mRNAs. Effects on target mRNA levels accumulate as the mRNA population approaches steady state, whereas effects on tail lengths peak for recently transcribed target mRNAs and then subside. Computational modeling of this phenomenon reveals that miRNAs cause not only accelerated deadenylation of their targets but also accelerated decay of short-tailed target molecules. This unanticipated effect of miRNAs largely prevents short-tailed target mRNAs from accumulating despite accelerated target deadenylation. The net result is a nearly imperceptible change to the steady-state tail-length distribution of targeted mRNAs.

The Dynamics of Cytoplasmic mRNA Metabolism.

For all but a few mRNAs, the dynamics of metabolism are unknown. Here, we developed an experimental and analytical framework for examining these dynamics for mRNAs from thousands of genes. mRNAs of mouse fibroblasts exit the nucleus with diverse intragenic and intergenic poly(A)-tail lengths. Once in the cytoplasm, they have a broad (1000-fold) range of deadenylation rate constants, which correspond to cytoplasmic lifetimes. Indeed, with few exceptions, degradation appears to occur primarily through deadenylation-linked mechanisms, with little contribution from either endonucleolytic cleavage or deadenylation-independent decapping. Most mRNA molecules degrade only after their tail lengths fall below 25 nt. Decay rate constants of short-tailed mRNAs vary broadly (1000-fold) and are larger for short-tailed mRNAs that have previously undergone more rapid deadenylation. This coupling helps clear rapidly deadenylated mRNAs, enabling the large range in deadenylation rate constants to impart a similarly large range in stabilities.

Determining and interpreting protein lifetimes in mammalian tissues.

The orchestration of protein production and degradation, and the regulation of protein lifetimes, play a central role in the majority of biological processes. Recent advances in proteomics have enabled the estimation of protein half-lives for thousands of proteins in vivo. What is the utility of these measurements, and how can they be leveraged to interpret the proteome changes occurring during development, aging, and disease? This opinion article summarizes leading technical approaches and highlights their strengths and weaknesses. We also disambiguate frequently used terminology, illustrate recent mechanistic insights, and provide guidance for interpreting and validating protein turnover measurements. Overall, protein lifetimes, coupled to estimates of protein levels, are essential for obtaining a deep understanding of mammalian biology and the basic processes defining life itself.

Coordination of rhythmic RNA synthesis and degradation orchestrates 24- and 12-h RNA expression patterns in mouse fibroblasts.

Circadian RNA expression is essential to ultimately regulate a plethora of downstream rhythmic biochemical, physiological, and behavioral processes. Both transcriptional and posttranscriptional mechanisms are considered important to drive rhythmic RNA expression; however, the extent to which each regulatory process contributes to the rhythmic RNA expression remains controversial. To systematically address this, we monitored RNA dynamics using metabolic RNA labeling technology during a circadian cycle in mouse fibroblasts. We find that rhythmic RNA synthesis is the primary contributor of 24-h RNA rhythms, while rhythmic degradation is more important for 12-h RNA rhythms. These rhythms were predominantly regulated by Bmal1 and/or the core clock mechanism, and the interplay between rhythmic synthesis and degradation has a significant impact in shaping rhythmic RNA expression patterns. Interestingly, core clock RNAs are regulated by multiple rhythmic processes and have the highest amplitude of synthesis and degradation, presumably critical to sustain robust rhythmicity of cell-autonomous circadian rhythms. Our study yields invaluable insights into the temporal dynamics of both 24- and 12-h RNA rhythms in mouse fibroblasts.

Cytokine conjugation to enhance T cell therapy.

Adoptive T cell transfer (ACT) therapies suffer from a number of limitations (e.g., poor control of solid tumors), and while combining ACT with cytokine therapy can enhance effectiveness, this also results in significant side effects. Here, we describe a nanotechnology approach to improve the efficacy of ACT therapies by metabolically labeling T cells with unnatural sugar nanoparticles, allowing direct conjugation of antitumor cytokines onto the T cell surface during the manufacturing process. This allows local, concentrated activity of otherwise toxic cytokines. This approach increases T cell infiltration into solid tumors, activates the host immune system toward a Type 1 response, encourages antigen spreading, and improves control of aggressive solid tumors and achieves complete blood cancer regression with otherwise noncurative doses of CAR-T cells. Overall, this method provides an effective and easily integrated approach to the current ACT manufacturing process to increase efficacy in various settings.

The major surface protein of malaria sporozoites is GPI-anchored to the plasma membrane.

Glycosylphosphatidylinositol (GPI) anchor protein modification in Plasmodium species is well known and represents the principal form of glycosylation in these organisms. The structure and biosynthesis of GPI anchors of Plasmodium spp. has been primarily studied in the asexual blood stage of Plasmodium falciparum and is known to contain the typical conserved GPI structure of EtN-P-Man3GlcN-PI. Here, we have investigated the circumsporozoite protein (CSP) for the presence of a GPI anchor. CSP is the major surface protein of Plasmodium sporozoites, the infective stage of the malaria parasite. While it is widely assumed that CSP is a GPI-anchored cell surface protein, compelling biochemical evidence for this supposition is absent. Here, we employed metabolic labeling and mass-spectrometry-based approaches to confirm the presence of a GPI anchor in CSP. Biosynthetic radiolabeling of CSP with [3H]-palmitic acid and [3H]-ethanolamine, with the former being base-labile and therefore ester-linked, provided strong evidence for the presence of a GPI anchor on CSP, but these data alone were not definitive. To provide further evidence, immunoprecipitated CSP was analyzed for the presence of myo-inositol (a characteristic component of GPI anchor) using strong acid hydrolysis and GC-MS for highly sensitive and quantitative detection. The single ion monitoring (SIM) method for GC-MS analysis confirmed the presence of the myo-inositol component in CSP. Taken together, these data provide confidence that the long-assumed presence of a GPI anchor on this important parasite protein is correct.

bakR: uncovering differential RNA synthesis and degradation kinetics transcriptome-wide with Bayesian hierarchical modeling.

Differential expression analysis of RNA sequencing (RNA-seq) data can identify changes in cellular RNA levels, but provides limited information about the kinetic mechanisms underlying such changes. Nucleotide recoding RNA-seq methods (NR-seq; e.g., TimeLapse-seq, SLAM-seq, etc.) address this shortcoming and are widely used approaches to identify changes in RNA synthesis and degradation kinetics. While advanced statistical models implemented in user-friendly software (e.g., DESeq2) have ensured the statistical rigor of differential expression analyses, no such tools that facilitate differential kinetic analysis with NR-seq exist. Here, we report the development of Bayesian analysis of the kinetics of RNA (bakR; https:// github.com/simonlabcode/bakR), an R package to address this need. bakR relies on Bayesian hierarchical modeling of NR-seq data to increase statistical power by sharing information across transcripts. Analyses of simulated data confirmed that bakR implementations of the hierarchical model outperform attempts to analyze differential kinetics with existing models. bakR also uncovers biological signals in real NR-seq data sets and provides improved analyses of existing data sets. This work establishes bakR as an important tool for identifying differential RNA synthesis and degradation kinetics.

Dissecting the spatiotemporal diversity of adult neural stem cells.

Adult stem cells are important for tissue turnover and regeneration. However, in most adult systems it remains elusive how stem cells assume different functional states and support spatially patterned tissue architecture. Here, we dissected the diversity of neural stem cells in the adult zebrafish brain, an organ that is characterized by pronounced zonation and high regenerative capacity. We combined single-cell transcriptomics of dissected brain regions with massively parallel lineage tracing and in vivo RNA metabolic labeling to analyze the regulation of neural stem cells in space and time. We detected a large diversity of neural stem cells, with some subtypes being restricted to a single brain region, while others were found globally across the brain. Global stem cell states are linked to neurogenic differentiation, with different states being involved in proliferative and non-proliferative differentiation. Our work reveals principles of adult stem cell organization and establishes a resource for the functional manipulation of neural stem cell subtypes.

An Introduction to Analytical Challenges, Approaches, and Applications in Mass Spectrometry-Based Secretomics.

The active release of proteins into the extracellular space and the proteolytic cleavage of cell surface proteins are key processes that coordinate and fine-tune a multitude of physiological functions. The entirety of proteins that fulfill these extracellular tasks are referred to as the secretome and are of special interest for the investigation of biomarkers of disease states and physiological processes related to cell-cell communication. LC-MS-based proteomics approaches are a valuable tool for the comprehensive and unbiased characterization of this important subproteome. This review discusses procedures, opportunities, and limitations of mass spectrometry-based secretomics to better understand and navigate the complex analytical landscape for studying protein secretion in biomedical science.

Cell type-specific labeling of newly synthesized proteins by puromycin inactivation.

Puromycin and its derivative O-propargyl puromycin (OPP) have recently found widespread use in detecting nascent proteins. Use of these metabolic labels in complex mixtures of cells leads to indiscriminate tagging of nascent proteomes independent of cell type. Here, we show how a widely used mammalian selection marker, puromycin N-acetyltransferase, can be repurposed for cell-specific metabolic labeling. This approach, which we named puromycin inactivation for cell-selective proteome labeling (PICSL), is based on efficient inactivation of puromycin or OPP in cells expressing puromycin N-acetyltransferase and detection of translation in other cell types. Using cocultures of neurons and glial cells from the rat brain cortex, we show the application of PICSL for puromycin immunostaining, Western blot, and mass spectrometric identification of nascent proteins. By combining PICSL and OPP-mediated proteomics, cell type-enriched proteins can be identified based on reduced OPP labeling in the cell type of interest.

Metabolic Labeling and Digital Microfluidic Single-Cell Sequencing for Single Bacterial Genotypic-Phenotypic Analysis.

Accurate assessment of phenotypic and genotypic characteristics of bacteria can facilitate comprehensive cataloguing of all the resistance factors for better understanding of antibiotic resistance. However, current methods primarily focus on individual phenotypic or genotypic profiles across different colonies. Here, a Digital microfluidic-based automated assay for whole-genome sequencing of single-antibiotic-resistant bacteria is reported, enabling Genotypic and Phenotypic Analysis of antibiotic-resistant strains (Digital-GPA). Digital-GPA can efficiently isolate and sequence antibiotic-resistant bacteria illuminated by fluorescent D-amino acid (FDAA)-labeling, producing high-quality single-cell amplified genomes (SAGs). This enables identifications of both minor and major mutations, pinpointing substrains with distinctive resistance mechanisms. Digital-GPA can directly process clinical samples to detect and sequence resistant pathogens without bacterial culture, subsequently provide genetic profiles of antibiotic susceptibility, promising to expedite the analysis of hard-to-culture or slow-growing bacteria. Overall, Digital-GPA opens a new avenue for antibiotic resistance analysis by providing accurate and comprehensive molecular profiles of antibiotic resistance at single-cell resolution.

Probing Glycerolipid Metabolism using a Caged Clickable Glycerol-3-Phosphate Probe.

In this study, we present the probe SATE-G3P-N3 as a novel tool for metabolic labeling of glycerolipids (GLs) to investigate lipid metabolism in yeast cells. By introducing a clickable azide handle onto the glycerol backbone, this probe enables general labeling of glycerolipids. Additionally, this probe contains a caged phosphate moiety at the glycerol sn-3 position to not only facilitate probe uptake by masking negative charge but also to bypass the phosphorylation step crucial for initiating phospholipid synthesis, thereby enhancing phospholipid labeling. The metabolic labeling activity of the probe was thoroughly assessed through cellular fluorescence microscopy, mass spectrometry (MS), and thin-layer chromatography (TLC) experiments. Fluorescence microscopy analysis demonstrated successful incorporation of the probe into yeast cells, with labeling predominantly localized at the plasma membrane. LCMS analysis confirmed metabolic labeling of various phospholipid species (PC, PS, PA, PI, and PG) and neutral lipids (MAG, DAG, and TAG), and GL labeling was corroborated by TLC. These results showcased the potential of the SATE-G3P-N3 probe in studying GL metabolism, offering a versatile and valuable approach to explore the intricate dynamics of lipids in yeast cells.

Photo-affinity and Metabolic Labeling Probes Based on the Opioid Alkaloids.

The opioids are powerful analgesics yet possess contingencies that can lead to opioid-use disorder. Chemical probes derived from the opioid alkaloids can provide deeper insight into the molecular interactions in a cellular context. Here, we designed and developed photo-click morphine (PCM-2) as a photo-affinity probe based on morphine and dialkynyl-acetyl morphine (DAAM) as a metabolic acetate reporter based on heroin. Application of these probes to SH-SY5Y, HEK293T, and U2OS cells revealed that PCM-2 and DAAM primarily localize to the lysosome amongst other locations inside the cell by confocal microscopy and chemical proteomics. Interaction site identification by mass spectrometry revealed the mitochondrial phosphate carrier protein, solute carrier family 25 member 3, SLC25A3, and histone H2B as acylation targets of DAAM. These data illustrate the utility of chemical probes to measure localization and protein interactions in a cellular context and will inform the design of probes based on the opioids in the future.

Global SLAM-seq for accurate mRNA decay determination and identification of NMD targets.

Gene expression analysis requires accurate measurements of global RNA degradation rates, earlier problematic with methods disruptive to cell physiology. Recently, metabolic RNA labeling emerged as an efficient and minimally invasive technique applied in mammalian cells. Here, we have adapted SH-linked alkylation for the metabolic sequencing of RNA (SLAM-seq) for a global mRNA stability study in yeast using 4-thiouracil pulse-chase labeling. We assign high-confidence half-life estimates for 67.5% of expressed ORFs, and measure a median half-life of 9.4 min. For mRNAs where half-life estimates exist in the literature, their ranking order was in good agreement with previous data, indicating that SLAM-seq efficiently classifies stable and unstable transcripts. We then leveraged our yeast protocol to identify targets of the nonsense-mediated decay (NMD) pathway by measuring the change in RNA half-lives, instead of steady-state RNA level changes. With SLAM-seq, we assign 580 transcripts as putative NMD targets, based on their measured half-lives in wild-type and upf3Δ mutants. We find 225 novel targets, and observe a strong agreement with previous reports of NMD targets, 61.2% of our candidates being identified in previous studies. This indicates that SLAM-seq is a simpler and more economic method for global quantification of mRNA half-lives. Our adaptation for yeast yielded global quantitative measures of the NMD effect on transcript half-lives, high correlation with RNA half-lives measured previously with more technically challenging protocols, and identification of novel NMD regulated transcripts that escaped prior detection.

Protein turnover models for LC-MS data of heavy water metabolic labeling.

Protein turnover is vital for cellular functioning and is often associated with the pathophysiology of a variety of diseases. Metabolic labeling with heavy water followed by liquid chromatography coupled to mass spectrometry is a powerful tool to study in vivo protein turnover in high throughput and large scale. Heavy water is a cost-effective and easy to use labeling agent. It labels all nonessential amino acids. Due to its toxicity in high concentrations (20% or higher), small enrichments (8% or smaller) of heavy water are used with most organisms. The low concentration results in incomplete labeling of peptides/proteins. Therefore, the data processing is more challenging and requires accurate quantification of labeled and unlabeled forms of a peptide from overlapping mass isotopomer distributions. The work describes the bioinformatics aspects of the analysis of heavy water labeled mass spectral data, available software tools and current challenges and opportunities.

Turnover and replication analysis by isotope labeling (TRAIL) reveals the influence of tissue context on protein and organelle lifetimes.

The lifespans of proteins range from minutes to years within mammalian tissues. Protein lifespan is relevant to organismal aging, as long-lived proteins accrue damage over time. It is unclear how protein lifetime is shaped by tissue context, where both cell turnover and proteolytic degradation contribute to protein turnover. We develop turnover and replication analysis by 15 N isotope labeling (TRAIL) to quantify protein and cell lifetimes with high precision and demonstrate that cell turnover, sequence-encoded features, and environmental factors modulate protein lifespan across tissues. Cell and protein turnover flux are comparable in proliferative tissues, while protein turnover outpaces cell turnover in slowly proliferative tissues. Physicochemical features such as hydrophobicity, charge, and disorder influence protein turnover in slowly proliferative tissues, but protein turnover is much less sequence-selective in highly proliferative tissues. Protein lifetimes vary nonrandomly across tissues after correcting for cell turnover. Multiprotein complexes such as the ribosome have consistent lifetimes across tissues, while mitochondria, peroxisomes, and lipid droplets have variable lifetimes. TRAIL can be used to explore how environment, aging, and disease affect tissue homeostasis.