Technical Variation Research Articles

Variation in the metagenomic analysis of fecal microbiome composition calls for a standardized operating approach.

The reproducibility in microbiome studies is limited due to the lack of one gold-standard operating procedure. The aim of this study was to examine the impact of protocol variations on microbiome composition using metagenomic data sets from a single center. We assessed the variation in a data set consisted of 2,722 subjects, including 9 subcohorts harboring healthy subjects and patients with various disorders, such as inflammatory bowel disease, colorectal cancer, and type 2 diabetes. Two different DNA extraction kits, with or without lyticase, and two sample storage methods were compared. Our results indicated that DNA extraction had the largest impact on gut microbiota diversity among all host factors and sample operating procedures. Healthy subjects matched by age, body mass index, and sample operating methods exhibited reduced, yet significant differences (PERMANOVA, P < 0.05) in gut microbiota composition across studies. The variations contributed by DNA extraction were primarily driven by different recovery efficiency of gram-positive bacteria, e.g., phyla Firmicutes and Actinobacteria. This was further confirmed by a parallel comparison of fecal samples from five healthy subjects and a standard mock community. In addition, the DNA extraction method influenced DNA biomass, quality, and the detection of specific lineage-associated diseases. Sample operating approach and batch effects should be considered for cohorts with large sample size or longitudinal cohorts to ensure that source data were appropriately generated and analyzed. Comparison between samples processed with inconsistent methods should be dealt with caution. This study will promote the establishment of a sample operating standard to enhance our understanding of microbiome and translating in clinical practice.IMPORTANCEThe reproducibility of human gut microbiome studies has been suboptimal across cohorts and study design choices. One possible reason for the disagreement is the introduction of systemic biases due to differences in methodologies. In our study, we utilized microbial metagenomic data sets from 2,722 fecal samples generated from a single research center to examine the extent to which sample storage and DNA extraction influence the quantification of microbial composition and compared this variable with other sources of technical and biological variation. Our research highlights the impact of DNA extraction methods when analyzing microbiome data and suggests that the microbiome profile may be influenced by differences in the extraction efficiency of bacterial species. With metagenomics sequencing being increasingly used in clinical biology, our findings provide insight into the challenges using metagenomics sequencing in clinical diagnostics, where the detection of certain species and its abundance relative to a "healthy reference" is key.

On the identification of differentially-active transcription factors from ATAC-seq data.

ATAC-seq has emerged as a rich epigenome profiling technique, and is commonly used to identify Transcription Factors (TFs) underlying given phenomena. A number of methods can be used to identify differentially-active TFs through the accessibility of their DNA-binding motif, however little is known on the best approaches for doing so. Here we benchmark several such methods using a combination of curated datasets with various forms of short-term perturbations on known TFs, as well as semi-simulations. We include both methods specifically designed for this type of data as well as some that can be repurposed for it. We also investigate variations to these methods, and identify three particularly promising approaches (a chromVAR-limma workflow with critical adjustments, monaLisa and a combination of GC smooth quantile normalization and multivariate modeling). We further investigate the specific use of nucleosome-free fragments, the combination of top methods, and the impact of technical variation. Finally, we illustrate the use of the top methods on a novel dataset to characterize the impact on DNA accessibility of TRAnscription Factor TArgeting Chimeras (TRAFTAC), which can deplete TFs-in our case NFkB-at the protein level.

Validation and quantification of peptide antigens presented on MHCs using SureQuant.

Vaccines and immunotherapies that target peptide-major histocompatibility complexes (peptide-MHCs) have the potential to address multiple unmet medical needs in cancer and infectious disease. Designing vaccines and immunotherapies to target peptide-MHCs requires accurate identification of target peptides in infected or cancerous cells or tissue, and may require absolute or relative quantification to identify abundant targets and measure changes in presentation under different treatment conditions. Internal standard parallel reaction monitoring (also known as 'SureQuant') can be used to validate and/or quantify MHC peptides previously identified by using untargeted methods such as data-dependent acquisition. SureQuant MHC has three main use cases: (i) conclusive confirmation of the identities of putative MHC peptides via comparison with an internal synthetic stable isotope labeled (SIL) peptide standard; (ii) accurate relative quantification by using pre-formed heavy isotope-labeled peptide-MHC complexes (hipMHCs) containing SIL peptides as internal controls for technical variation; and (iii) absolute quantification of each target peptide by using different amounts of hipMHCs loaded with synthetic peptides containing one, two or three SIL amino acids to provide an internal standard curve. Absolute quantification can help determine whether the abundance of a peptide-MHC is sufficient for certain therapeutic modalities. SureQuant MHC therefore provides unique advantages for immunologists seeking to confidently validate antigenic targets and understand the dynamics of the MHC repertoire. After synthetic standards are ordered (3-4 weeks), this protocol can be carried out in 3-4 days and is suitable for individuals with mass spectrometry experience who are comfortable with customizing instrument methods.

Performance evaluation of image co-registration methods in photoacoustic mesoscopy of the vasculature

Objective:The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion. To better inform the choice of co-registration algorithms, we compared five open-source methods, in physiological and pathological tissues, with the aim of aligning evolving vascular networks in tumours imaged over growth at different time-points.Approach:Co-registration techniques were applied to 3D vascular images acquired with photoacoustic mesoscopy from murine ears and breast cancer patient-derived xenografts, at a fixed time-point and longitudinally. Images were pre-processed and segmented using an unsupervised generative adversarial network. To compare co-registration quality in different settings, pairs of fixed and moving intensity images and/or segmentations were fed into five methods split into the following categories: affine intensity-based using 1)mutual information (MI) or 2)normalised cross-correlation (NCC) as optimisation metrics, affine shape-based using 3)NCC applied to distance-transformed segmentations or 4)iterative closest point algorithm, and deformable weakly supervised deep learning-based using 5)LocalNet co-registration. Percent-changes in Dice coefficients, surface distances, MI, structural similarity index measure and target registration errors were evaluated.Main results:Co-registration using MI or NCC provided similar alignment performance, better than shape-based methods. LocalNet provided accurate co-registration of substructures by optimising subfield deformation throughout the volumes, outperforming other methods, especially in the longitudinal breast cancer xenograft dataset by minimising target registration errors.Significance:We showed the feasibility of co-registering repeatedly or longitudinally imaged vascular networks in photoacoustic mesoscopy, taking a step towards longitudinal quantitative characterisation of these complex structures. These tools open new outlooks for monitoring tumour angiogenesis at the meso-scale and for quantifying treatment-induced co-localised alterations in the vasculature.

Inferring post-transcriptional regulation within and across cell types in human testis.

Single-cell tissue atlases commonly use RNA abundances as surrogates for protein abundances. Yet, protein abundance also depends on the regulation of protein synthesis and degradation rates. To estimate the contributions of such post transcriptional regulation, we quantified the proteomes of 5,883 single cells from human testis using 3 distinct mass spectrometry methods (SCoPE2, pSCoPE, and plexDIA). To distinguish between biological and technical factors contributing to differences between protein and RNA levels, we developed BayesPG , a Bayesian model of transcript and protein abundance that systematically accounts for technical variation and infers biological differences. We use BayesPG to jointly model RNA and protein data collected from 29,709 single cells across different methods and datasets. BayesPG estimated consensus mRNA and protein levels for 3,861 gene products and quantified the relative protein-to-mRNA ratio (rPTR) for each gene across six distinct cell types in samples from human testis. About 28% of the gene products exhibited significant differences at protein and RNA levels and contributed to about 1, 500 significant GO groups. We observe that specialized and context specific functions, such as those related to spermatogenesis are regulated after transcription. Among hundreds of detected post translationally modified peptides, many show significant abundance differences across cell types. Furthermore, some phosphorylated peptides covary with kinases in a cell-type dependent manner, suggesting cell-type specific regulation. Our results demonstrate the potential of inferring protein regulation in from single-cell proteogenomic data and provide a generalizable model, BayesPG , for performing such analyses.

Alternative Method for Obtaining Human-Induced Pluripotent Stem Cell Lines and Three-Dimensional Growth: A Simplified, Passage-Free Approach that Minimizes Labor.

Induced pluripotent stem cells (iPSCs) hold significant promise for numerous applications in regenerative medicine, disease modeling, and drug discovery. However, the conventional workflow for iPSC generation, with cells grown under two-dimensional conditions, presents several challenges, including the need for specialized scientific skills such as morphologically assessing and picking colonies and removing differentiated cells during the establishment phase. Furthermore, maintaining established iPSCs in three-dimensional culture systems, while offering scalability, necessitates an enzymatic dissociation step for their further growth in a complex and time-consuming protocol. In this study, we introduce a novel approach to address these challenges by reprogramming somatic cells grown under three-dimensional conditions as spheres using a bioreactor, thereby eliminating the need for two-dimensional culture and colony picking. The iPSCs generated in this study were maintained under three-dimensional conditions simply by transferring spheres to the next bioreactor, without the need for an enzymatic dissociation step. This streamlined method simplifies the workflow, reduces technical variability and labor, and paves the way for future advancements in iPSC research and its wider applications. Key features • Establishment of induced pluripotent stem cells in a three-dimensional environment. • Maintenance and cryopreservation of iPSCs without the need for a dissociation step.

Establishing Consensus for Mohs Micrographic Surgical Techniques in the Treatment of Melanoma in Situ for Future Clinical Trials: A Modified Delphi Study.

Mohs micrographic surgery (MMS) is a promising treatment modality for melanoma in situ (MIS). However, variations in surgical technique limit the generalizability of existing data and may impede future study of MMS in clinical trials. A modified Delphi method was selected to establish consensus on optimal MMS techniques for treating MIS in future clinical trials. The Delphi method was selected due to the limited current data, the wide range of techniques used in the field, and the intention to establish a standardized technique for future clinical trials. A literature review and interviews with experienced MMS surgeons were performed to identify dimensions of the MMS technique for MIS that (1) likely impacted costs or outcomes of the procedure, and (2) showed significant variability between surgeons. A total of 8 dimensions of technical variation were selected. The Delphi process consisted of 2 rounds of voting and commentary, during which 44 expert Mohs surgeons across the United States rated their agreement with specific recommendations using a Likert scale. Five of eight recommendations achieved consensus in Round 1. All 3 of the remaining recommendations achieved consensus in Round 2. Techniques achieving consensus in Round 1 included the use of a starting peripheral margin of ≤5 mm, application of immunohistochemistry, frozen tissue processing, and resecting to the depth of subcutaneous fat. Consensus on the use of Wood's lamp, dermatoscope, and negative tissue controls was established in Round 2. This study generated 8 consensus recommendations intended to offer guidance for Mohs surgeons treating MIS. The adoption of these recommendations will promote standardization to facilitate comparisons of aggregate data in multicenter clinical trials.

A Mixed-Effect Kernel Machine Regression Model for Integrative Analysis of Alpha Diversity in Microbiome Studies.

Increasing evidence suggests that human microbiota plays a crucial role in many diseases. Alpha diversity, a commonly used summary statistic that captures the richness and/or evenness of the microbial community, has been associated with many clinical conditions. However, individual studies that assess the association between alpha diversity and clinical conditions often provide inconsistent results due to insufficient sample size, heterogeneous study populations and technical variability. In practice, meta-analysis tools have been applied to integrate data from multiple studies. However, these methods do not consider the heterogeneity caused by sequencing protocols, and the contribution of each study to the final model depends mainly on its sample size (or variance estimate). To combine studies with distinct sequencing protocols, a robust statistical framework for integrative analysis of microbiome datasets is needed. Here, we propose a mixed-effect kernel machine regression model to assess the association of alpha diversity with a phenotype of interest. Our approach readily incorporates the study-specific characteristics (including sequencing protocols) to allow for flexible modeling of microbiome effect via a kernel similarity matrix. Within the proposed framework, we provide three hypothesis testing approaches to answer different questions that are of interest to researchers. We evaluate the model performance through extensive simulations based on two distinct data generation mechanisms. We also apply our framework to data from HIV reanalysis consortium to investigate gut dysbiosis in HIV infection.

Understanding the significance of oxygen tension on the biology of Plasmodium falciparum blood stages: From the human body to the laboratory

Plasmodium falciparum undergoes sequestration within deep tissues of the human body, spanning multiple organ systems with differing oxygen (O2) concentrations. The parasite is exposed to an even greater range of O2 concentrations as it transitions from the human to the mosquito host, suggesting a high level of plasticity as it navigates these different environments. In this review, we explore factors that may contribute to the parasite’s response to different environmental O2 concentrations, recognizing that there are likely multiple pieces to this puzzle. We first review O2-sensing mechanisms, which exist in other apicomplexans such as Toxoplasma gondii and consider whether similar systems could exist in Plasmodium. Next, we review morphological and functional changes in P. falciparum’s mitochondrion during the asexual-to-sexual stage transition and discuss how these changes overlap with the parasite’s access to O2. We then delve into reactive oxygen species (ROS) as ROS production is influenced by O2 availability and oxidative stress impacts Plasmodium intraerythrocytic development. Lastly, given that the primary role of the red blood cell (RBC) is to deliver O2 throughout the body, we discuss how changes in the oxygenation status of hemoglobin, the RBC’s O2-carrying protein and key nutrient for Plasmodium, could also potentially impact the parasite’s growth during intraerythrocytic development. This review also highlights studies that have investigated P. falciparum biology under varying O2 concentrations and covers technical aspects related to P. falciparum cultivation in the lab, focusing on sources of technical variation that could alter the amount of dissolved O2 encountered by cells during in vitro experiments. Lastly, we discuss how culture systems can better replicate in vivo heterogeneity with respect to O2 gradients, propose ideas for further research in this area, and consider translational implications related to O2 and malaria.

Age-dependent changes in cytochrome P450 abundance and composition in human liver.

Cytochrome P450 (CYP) superfamily represents the major drug metabolizing enzymes responsible for metabolizing over 65% of therapeutic drugs, including those for pediatric use. CYP-ontogeny based physiologically-based pharmacokinetic (PBPK) models have emerged as useful tools to mechanistically extrapolate adult pharmacokinetic data to children. However, these models integrate physiological differences in pediatric population including age-dependent differences in the abundances of CYP enzymes. Conventionally, developmental changes in CYP enzymes have been reported using protein abundance and activity data from subcellular fractions such as microsomes, which is prone to high technical variability. Similarly, the available pediatric pharmacokinetic data suffer from the lack of specific CYP substrates, especially in younger children. In the present study, we utilized viable hepatocytes from 50 pediatric (age, day 1- 18 yr) and 8 adult human donors and carried out global proteomics-based quantification of all major hepatic CYP enzymes, including orphan enzymes that have not been studied previously. While CYPs 2B6, 3A5, 4A11, 4F3, and 4V2 did not show significant association with age, all other quantified isoforms either increased or decreased with age. CYPs 1A2, 2C8, 2C18, and 2C19 were absent or barely detected in the neonatal group, while CYP3A7 was the highest in this group. The >1-2 yr age-group showed the highest total abundance of all CYP enzymes. The age-dependent differences in CYP enzymes reported in this study can be used to develop ontogeny-based PBPK models, which in turn can help improve pediatric dose-prediction based on adult dosing, leading to safer drug pharmacology in children. Significance Statement We quantified age-dependent differences in the abundances of hepatic CYP enzymes using a large set of viable pediatric and adult hepatocytes using quantitative global proteomics. We report for the first time the ontogeny in the abundance of CYP enzymes in human hepatocytes, especially, orphan CYPs 20A1, 27A1, 51A1, 7B1, and 8B1 and the CYP4 subfamily of enzymes. Our study provides important data about CYP ontogeny that can be used for the better prediction of pediatric pharmacokinetics using physiologically-based pharmacokinetic modeling.

Economic Efficiency of Potato Production by Smallholder Farmers in West Arsi Zone, Oromia Region Ethiopia

This study investigated Economic Efficiency of Potato Production in West Arsi Zone, Oromia Region, Ethiopia. Structured questionnaires were used to collect data from 117 respondents randomly selected from designated locations in West Arsi Zone. A stochastic production frontier function was fitted to the sample households. The result revealed that the mean TE, AE and EE was about 75.60%, 91.41% and 69.07% of for potato production. The sum of the partial elasticity of all inputs were 1.17 for Potato indicating an increase in all inputs at the sample mean by one percent increase by 1.17%. This indicates that the production function is characterized by increasing returns to scale productions. The result of Tobit model estimation indicated that the technical efficiency of Potato production in West Arsi Zone is significantly influenced by the variables potato farming experience, education level, social participation and Extension contact affect efficiency positively while, distance to FTC affect technical efficiency negatively. The mean potato yield difference between sample farmer due to technical efficiency variation was 31.04 qt per ha. District office of Agriculture, stockholders and concerned bodies should focus on farmers experience sharing, providing technical support and farmers practice different social participation to improve his/her income could jointly contribute to the improvement in efficiency of Potato farmers in West Arsi Zone.

Clinical comparison of aspiration to stentriever-mediated aspiration thrombectomy for M2 occlusions

ObjectiveWhile revascularization for M2 occlusions is generally recommended and considered beneficial, the optimal approach (aspiration vs stentriever/combined) is less well defined in the literature. We sought to compare outcomes after thrombectomy with manual aspiration thrombectomy alone (MAT) or stentriever-mediated aspiration thrombectomy (SMAT) Materials and MethodsTo circumvent inter-operator technical variability, patients underwent thrombectomy for M2 occlusions by a single operator and were stratified by first pass approach: manual aspiration thrombectomy (MAT/aspiration alone) and stentriever-mediated manual aspiration thrombectomy (SMAT/combination). Efficacy outcomes included good reperfusion (mTICI score ≥2b) and a favorable 90-day functional outcome (mRS score of ≤2). Safety outcomes included 90-day mortality and symptomatic intracranial hemorrhage. ResultsOne hundred three patients were identified: 57 underwent MAT whereas 46 underwent SMAT. Good reperfusion (TICI 2b or greater) was comparable between groups (93.5 % vs. 87.7 %, P=0.33). The intracranial hemorrhage rate was higher with SMAT compared to MAT (13 % vs. 1.8 %, P=0.04). Puncture-to-recanalization time was longer in SMAT (34.4 vs. 19.9 minutes, P<0.001). In multivariable analysis, complete or good reperfusion was associated with shorter puncture-recanalization time (adjusted odds ratio [aOR], 0.85) or less total passes (aOR, 0.58), respectively. In a propensity score matched analysis of 66 patients with comparable baseline and technical efficiency, the safety outcomes were not different between SMAT and MAT groups. ConclusionsSMAT compared with MAT did not result in an improved revascularization rate or functional outcome, while it was related to more hemorrhagic complications and mortality. Given cost and time savings with manual aspiration alone, this approach may be optimal as first line for M2 occlusions.

Collaborative Structure-Preserved Missing Data Imputation for Single-Cell RNA-Seq Clustering.

Clustering of the single-cell RNA-seq (scRNA-seq) transcriptome profiles is able to identify cell types, which is beneficial to improve the understanding of disease progression. However, in practice, the single-cell expression data often contains a significant number of missing values as a result of technical variability. Missing data is a critical challenge in scRNA-seq clustering analysis since the unknown value does not reflect the underlying true expression level and makes it difficult to discovering cell types by applying clustering algorithms directly. Various approaches have been developed to overcome missing data issue in scRNA-seq clustering. Most of them recover missing expression values by borrowing observed data from similar cells or synthesizing data via generative adversarial networks. Such that the biologically meaningful cluster structure has not been sufficiently exploited. In this work, we introduce ColImpute, a collaborative structure-preserved missing data imputation approach for the scRNA-seq clustering. Specifically, a cluster structure-preserved imputation module and a subspace clustering module, which respectively perform missing data imputation and cell subtypes identification, are integrated into a unified optimization framework to train the two networks in a collaborative manner. Consequently, the clustering module effectively contributes cluster-structure information to guide the trainning process of the missing data imputation module. Simultaneously, the cluster structure-preserved imputation module reciprocally enhances the performance of the clustering module by generating more precise recovered samples. Promising experimental results show that the proposed method is effective for both the data imputation and the cell types identification.

A novel multi-objective decision-making model to determine optimum resource and capacity configuration for hybrid electricity generation systems: A comparative case study in Türkiye

The rapid depletion of fossil fuels, their negative environmental impacts due to harmful carbon emissions, and the drawbacks of the sole use of intermittent renewable energy resources on system reliability necessitate the use of more advanced energy system technologies. As a result, hybrid electricity generation systems offer an innovative alternative to conventional energy systems. However, the technical complexity and variability of these systems require the application of multi-objective optimization techniques to determine the optimal sizing and system design. Therefore, in this study, we suggest a multi-objective decision-making (MODM) model that involves five conflicting objectives to find the optimal resource configuration and auxiliary source capacity allocation within the framework of the multi-source electricity production method in Türkiye. The MODM model aims to maximize the utilization of resource potentials and the jobs created, while simultaneously minimizing the levelized cost of energy (LCOE), construction duration, and carbon emissions. The MODM model was solved using fmincon and fgoalattain multi-objective optimization algorithms using the Goal Attainment Method in MATLAB and the model results were subsequently compared to the real-world data. According to the model results, the optimal capacity to be allocated for the auxiliary source units is 13,749.58 MWm, while the capacity allocated according to the real-world data as of March 2024 is 2495.49 MWm. In contrast to the model's prediction of 90.8 % solar-based auxiliary unit installations, 99.8 % of auxiliary units are based on solar PV in the actual case. Furthermore, hydro or geothermal-based auxiliary sources are not allocated any capacity, which is consistent with the actual situation. The capacity allocated for the wind (primary source) + solar (auxiliary source) configuration is 76.2 %, whereas in reality, it accounts for 65.6 % of the total installations. Consequently, the model findings suggest that the maximum capacity allocation is for the wind (primary source) + solar (auxiliary source) type hybrid electricity generation system.

On the identification of differentially-active transcription factors from ATAC-seq data.

ATAC-seq has emerged as a rich epigenome profiling technique, and is commonly used to identify Transcription Factors (TFs) underlying given phenomena. A number of methods can be used to identify differentially-active TFs through the accessibility of their DNA-binding motif, however little is known on the best approaches for doing so. Here we benchmark several such methods using a combination of curated datasets with various forms of short-term perturbations on known TFs, as well as semi-simulations. We include both methods specifically designed for this type of data as well as some that can be repurposed for it. We also investigate variations to these methods, and identify three particularly promising approaches (a chromVAR-limma workflow with critical adjustments, monaLisa and a combination of GC smooth quantile normalization and multivariate modeling). We further investigate the specific use of nucleosome-free fragments, the combination of top methods, and the impact of technical variation. Finally, we illustrate the use of the top methods on a novel dataset to characterize the impact on DNA accessibility of TRAnscription Factor TArgeting Chimeras (TRAFTAC), which can deplete TFs - in our case NFkB - at the protein level.

Developmental Expression of Drug Transporters and Conjugating Enzymes Involved in Enterohepatic Recycling: Implication for Pediatric Drug Dosing.

Around 50% of the drugs used in children have never been tested for safety and efficacy in this vulnerable population. Immature drug elimination pathways can lead to drug toxicity when pediatric doses are determined using empirical methods such as body-surface area or body-weight-normalized adult dosing. In the absence of clinical data, physiologically-based pharmacokinetic (PBPK) modeling has emerged as a useful tool to predict drug pharmacokinetics in children. These models utilize developmental physiological data, including age-dependent differences in the abundance of drug-metabolizing enzymes and transporters (DMET), to mechanistically extrapolate adult pharmacokinetic data to children. The reported abundance data of hepatic DMET proteins in subcellular fractions isolated from frozen tissue are prone to high technical variability. Therefore, we carried out the proteomics-based quantification of hepatic drug transporters and conjugating enzymes in 50 pediatric and 8 adult human hepatocyte samples. Out of the 34 studied proteins, 28 showed a significant increase or decrease with age. While MRP6, OAT7, and SULT1E1 were highest in < 1-year-old samples, the abundance of P-gp and UGT1A4 was negligible in < 1-year-old samples and increased significantly after 1 year of age. Incorporation of the age-dependent abundance data in PBPK models can help improve pediatric dose prediction, leading to safer drug pharmacotherapy in children.

Global transcription regulation revealed from dynamical correlations in time-resolved single-cell RNA sequencing

Single-cell transcriptomics reveals significant variations in transcriptional activity across cells. Yet, it remains challenging to identify mechanisms of transcription dynamics from static snapshots. It is thus still unknown what drives global transcription dynamics in single cells. We present a stochastic model of gene expression with cell size- and cell cycle-dependent rates in growing and dividing cells that harnesses temporal dimensions of single-cell RNA sequencing through metabolic labeling protocols and cel lcycle reporters. We develop a parallel and highly scalable approximate Bayesian computation method that corrects for technical variation and accurately quantifies absolute burst frequency, burst size, and degradation rate along the cell cycle at a transcriptome-wide scale. Using Bayesian model selection, we reveal scaling between transcription rates and cell size and unveil waves of gene regulation across the cell cycle-dependent transcriptome. Our study shows that stochastic modeling of dynamical correlations identifies global mechanisms of transcription regulation. A record of this paper's transparent peer review process is included in the supplemental information.

PARE: A framework for removal of confounding effects from any distance-based dimension reduction method.

Dimension reduction tools preserving similarity and graph structure such as t-SNE and UMAP can capture complex biological patterns in high-dimensional data. However, these tools typically are not designed to separate effects of interest from unwanted effects due to confounders. We introduce the partial embedding (PARE) framework, which enables removal of confounders from any distance-based dimension reduction method. We then develop partial t-SNE and partial UMAP and apply these methods to genomic and neuroimaging data. For lower-dimensional visualization, our results show that the PARE framework can remove batch effects in single-cell sequencing data as well as separate clinical and technical variability in neuroimaging measures. We demonstrate that the PARE framework extends dimension reduction methods to highlight biological patterns of interest while effectively removing confounding effects.

ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets.

CITE-seq enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm (https://github.com/yezhengSTAT/ADTnorm), a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to a published COVID-19 CITE-seq dataset allowed for identifying previously undetected disease-associated markers, illustrating a broad utility in biological applications.

Skin Surface Sebum Analysis by ESI-MS.

The skin surface is an important sample source that the metabolomics community has only just begun to explore. Alterations in sebum, the lipid-rich mixture coating the skin surface, correlate with age, sex, ethnicity, diet, exercise, and disease state, making the skin surface an ideal sample source for future noninvasive biomarker exploration, disease diagnosis, and forensic investigation. The potential of sebum sampling has been realized primarily via electrospray ionization mass spectrometry (ESI-MS), an ideal approach to assess the skin surface lipidome. However, a better understanding of sebum collection and subsequent ESI-MS analysis is required before skin surface sampling can be implemented in routine analyses. Challenges include ambiguity in definitive lipid identification, inherent biological variability in sebum production, and methodological, technical variability in analyses. To overcome these obstacles, avoid common pitfalls, and achieve reproducible, robust outcomes, every portion of the workflow-from sample collection to data analysis-should be carefully considered with the specific application in mind. This review details current practices in sebum sampling, sample preparation, ESI-MS data acquisition, and data analysis, and it provides important considerations in acquiring meaningful lipidomic datasets from the skin surface. Forensic researchers investigating sebum as a means for suspect elimination in lieu of adequate fingerprint ridge detail or database matches, as well as clinical researchers interested in noninvasive biomarker exploration, disease diagnosis, and treatment monitoring, can use this review as a guide for developing methods of best-practice.