MetaNorm: incorporating meta-analytic priors into normalization of NanoString nCounter data.

MOTIVATION: Non-informative or diffuse prior distributions are widely employed in Bayesian data analysis to maintain objectivity. However, when meaningful prior information exists and can be identified, using an informative prior distribution to accurately reflect current knowledge may lead to superior outcomes and great efficiency. RESULTS: We propose MetaNorm, a Bayesian algorithm for normalizing NanoString nCounter gene expression data. MetaNorm is based on RCRnorm, a powerful method designed under an integrated series of hierarchical models that allow various sources of error to be explained by different types of probes in the nCounter system. However, a lack of accurate prior information, weak computational efficiency, and instability of estimates that sometimes occur weakens the approach despite its impressive performance. MetaNorm employs priors carefully constructed from a rigorous meta-analysis to leverage information from large public data. Combined with additional algorithmic enhancements, MetaNorm improves RCRnorm by yielding more stable estimation of normalized values, better convergence diagnostics and superior computational efficiency. AVAILABILITY AND IMPLEMENTATION: R Code for replicating the meta-analysis and the normalization function can be found at github.com/jbarth216/MetaNorm.

Mapping the functional landscape of T cell receptor repertoires by single-T cell transcriptomics.

Many experimental and bioinformatics approaches have been developed to characterize the human T cell receptor (TCR) repertoire. However, the unknown functional relevance of TCR profiling hinders unbiased interpretation of the biology of T cells. To address this inadequacy, we developed tessa, a tool to integrate TCRs with gene expression of T cells to estimate the effect that TCRs confer on the phenotypes of T cells. Tessa leveraged techniques combining single-cell RNA-sequencing with TCR sequencing. We validated tessa and showed its superiority over existing approaches that investigate only the TCR sequences. With tessa, we demonstrated that TCR similarity constrains the phenotypes of T cells to be similar and dictates a gradient in antigen targeting efficiency of T cell clonotypes with convergent TCRs. We showed this constraint could predict a functional dichotomization of T cells postimmunotherapy treatment and is weakened in tumor contexts.

Bayesian multitask learning for medicine recommendation based on online patient reviews.

MOTIVATION: We propose a drug recommendation model that integrates information from both structured data (patient demographic information) and unstructured texts (patient reviews). It is based on multitask learning to predict review ratings of several satisfaction-related measures for a given medicine, where related tasks can learn from each other for prediction. The learned models can then be applied to new patients for drug recommendation. This is fundamentally different from most recommender systems in e-commerce, which do not work well for new customers (referred to as the cold-start problem). To extract information from review texts, we employ both topic modeling and sentiment analysis. We further incorporate variable selection into the model via Bayesian LASSO, which aims to filter out irrelevant features. To our best knowledge, this is the first Bayesian multitask learning method for ordinal responses. We are also the first to apply multitask learning to medicine recommendation. The sample code and data are made available at GitHub: https://github.com/thrushcyc-github/BMull. RESULTS: We evaluate the proposed method on two sets of drug reviews involving 17 depression/high blood pressure-related drugs. Overall, our method performs better than existing benchmark methods in terms of accuracy and AUC (area under the receiver operating characteristic curve). It is effective even with a small sample size and only a few available features, and more robust to possible noninformative covariates. Due to our model explainability, insights generated from our model may work as a useful reference for doctors. In practice, however, a final decision should be carefully made by combining the information from the proposed recommender with doctors' domain knowledge and past experience. AVAILABILITY AND IMPLEMENTATION: The sample code and data are publicly available at GitHub: https://github.com/thrushcyc-github/BMull.

AI-based digital pathology provides newer insights into lifestyle intervention-induced fibrosis regression in MASLD: An exploratory study.

BACKGROUND AND AIMS: Lifestyle intervention is the mainstay of therapy for metabolic dysfunction-associated steatohepatitis (MASH), and liver fibrosis is a key consequence of MASH that predicts adverse clinical outcomes. The placebo response plays a pivotal role in the outcome of MASH clinical trials. Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy with artificial intelligence analyses can provide an automated quantitative assessment of fibrosis features on a continuous scale called qFibrosis. In this exploratory study, we used this approach to gain insight into the effect of lifestyle intervention-induced fibrosis changes in MASH. METHODS: We examined unstained sections from paired liver biopsies (baseline and end-of-intervention) from MASH individuals who had received either routine lifestyle intervention (RLI) (n = 35) or strengthened lifestyle intervention (SLI) (n = 17). We quantified liver fibrosis with qFibrosis in the portal tract, periportal, transitional, pericentral, and central vein regions. RESULTS: About 20% (7/35) and 65% (11/17) of patients had fibrosis regression in the RLI and SLI groups, respectively. Liver fibrosis tended towards no change or regression after each lifestyle intervention, and this phenomenon was more prominent in the SLI group. SLI-induced liver fibrosis regression was concentrated in the periportal region. CONCLUSION: Using digital pathology, we could detect a more pronounced fibrosis regression with SLI, mainly in the periportal region. With changes in fibrosis area in the periportal region, we could differentiate RLI and SLI patients in the placebo group in the MASH clinical trial. Digital pathology provides new insight into lifestyle-induced fibrosis regression and placebo responses, which is not captured by conventional histological staging.

Detection of uridine diphosphate glucuronosyltransferase 1A1 for pancreatic cancer imaging and treatment via a "turn-on" fluorescent probe.

Uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) is expressed ubiquitously in cancer cells and can metabolize exogenous substances. Studies show higher UGT1A1 levels in pancreatic cancer cells than normal cells. Therefore, we need a method to monitor the activity level of UGT1A1 in pancreatic cancer cells and in vivo. Here, we report a fluorescent probe, BCy-panc, for UGT1A1 imaging in cells and in vivo. Compared with other molecular probes, this probe is readily prepared, with high selectivity and sensitivity for the detection of UGT1A1. Our results show that BCy-panc rapidly detects UGT1A1 in pancreatic cancer. In addition, there is an urgent need for evidence to clarify the relationship between UGT1A1 and pancreatic cancer development. The present investigation found that the increase of UGT1A1 by chrysin was effective in inducing apoptosis in pancreatic cancer cells. These results indicate that the synergistic effect of chrysin and cisplatin at the cellular level is superior to that of cisplatin alone. The UGT1A1 level may be a biomarker for early diagnosis of cancer. Meanwhile, UGT1A1 plays a crucial role in pancreatic cancer, and the combination of chrysin and cisplatin may provide effective ideas for pancreatic cancer treatment.

Clinical characteristics and molecular mechanisms underlying bladder cancer in individuals with spinal cord injury: a systematic review.

BACKGROUND: Patients with spinal cord injury have a relatively high risk for bladder cancer and often complicated with bladder cancer in advanced stages, and the degree of aggressiveness of malignancy is high. Most of the literature is based on disease clinical features while, our study reviews the clinical characteristics and molecular mechanisms of spinal cord injury patients with bladder cancer, so that it might help clinicians better recognize and manage these patients. METHOD: We searched PubMed, Web of Science and Embase, using retrieval type like ("Neurogenic Lower Urinary Tract Dysfunction" OR "Spinal cord injury" OR "Spinal Cord Trauma") AND ("bladder cancer" OR "bladder neoplasm" OR "bladder carcinoma" OR "Urinary Bladder Neoplasms" OR "Bladder Tumor"). In Web of Science, the retrieval type was searched as "Topic", and in PubMed and Embase, as "All Field". The methodological quality of eligible studies and their risk of bias were assessed using the Newcastle-Ottawa scale. This article is registered in PROSPERO with the CBD number: CRD42024508514. RESULT: In WOS, we searched 219 related papers, in PubMed, 122 and in Embase, 363. Thus, a total of 254 articles were included after passing the screening, within a time range between 1960 and 2023. A comprehensive analysis of the data showed that the mortality and incidence rates of bladder cancer in spinal cord injury patients were higher than that of the general population, and the most frequent pathological type was squamous cell carcinoma. In parallel to long-term urinary tract infection and indwelling catheterization, the role of molecules such as NO, MiR 1949 and Rb 1. was found to be crucial pathogenetically. CONCLUSION: This review highlights the risk of bladder cancer in SCI patients, comprehensively addressing the clinical characteristics and related molecular mechanisms. However, given that there are few studies on the molecular mechanisms of bladder cancer in spinal cord injury, further research is needed to expand the understanding of the disease.

Rhoptry proteins affect the placental barrier in the context of Toxoplasma gondii infection: Signaling pathways and functions.

Toxoplasma gondii is an opportunistic and pathogenic obligate intracellular parasitic protozoan that is widespread worldwide and can infect most warm-blooded animals, seriously endangering human health and affecting livestock production. Toxoplasmosis caused by T. gondii infection has different clinical manifestations, which are mainly determined by the virulence of T. gondii and host differences. Among the manifestations of this condition, abortion, stillbirth, and fetal malformation can occur if a woman is infected with T. gondii in early pregnancy. Here, we discuss how the T. gondii rhoptry protein affects host pregnancy outcomes and speculate on the related signaling pathways involved. The effects of rhoptry proteins of T. gondii on the placental barrier are complex. Rhoptry proteins not only regulate interferon-regulated genes (IRGs) to ensure the survival of parasites in activated cells but also promote the spread of worms in tissues and the invasive ability of the parasites. The functions of these rhoptry proteins and the associated signaling pathways highlight relevant mechanisms by which Toxoplasma crosses the placental barrier and influences fetal development and will guide future studies to uncover the complexity of the host-pathogen interactions.

Variable Selection in Bayesian Multiple Instance Regression using Shotgun Stochastic Search.

In multiple instance learning (MIL), a bag represents a sample that has a set of instances, each of which is described by a vector of explanatory variables, but the entire bag only has one label/response. Though many methods for MIL have been developed to date, few have paid attention to interpretability of models and results. The proposed Bayesian regression model stands on two levels of hierarchy, which transparently show how explanatory variables explain and instances contribute to bag responses. Moreover, two selection problems are simultaneously addressed; the instance selection to find out the instances in each bag responsible for the bag response, and the variable selection to search for the important covariates. To explore a joint discrete space of indicator variables created for selection of both explanatory variables and instances, the shotgun stochastic search algorithm is modified to fit in the MIL context. Also, the proposed model offers a natural and rigorous way to quantify uncertainty in coefficient estimation and outcome prediction, which many modern MIL applications call for. The simulation study shows the proposed regression model can select variables and instances with high performance (AUC greater than 0.86), thus predicting responses well. The proposed method is applied to the musk data for prediction of binding strengths (labels) between molecules (bags) with different conformations (instances) and target receptors. It outperforms all existing methods, and can identify variables relevant in modeling responses.

Predictive value of TCCD and regional cerebral oxygen saturation for detecting early postoperative brain injury.

OBJECTIVE: This study aims to analyze the risk factors for early postoperative brain injury in patients undergoing cardiovascular surgery and explore the predictive value of transcranial color Doppler (TCCD) and regional cerebral oxygen saturation (rSO2) for detecting early postoperative brain injury in cardiovascular surgery patients. METHODS: A total of 55 patients undergoing cardiovascular surgery with cardiopulmonary bypass in Changzhou No.2 The People's Hospital of Nanjing Medical University were included in this study. Neuron-specific enolase (NSE) concentration was measured 24 h after operation. Patients were divided into brain injury (NSE ≥ 16.3 ng/mL) and normal (0 < NSE < 16.3 ng/mL) groups according to the measured NSE concentration. The clinical outcomes between the two groups were compared, including decreased rSO2 and cerebral blood flow (as measured by TCCD) levels. The risk factors of early postoperative brain injury were analyzed by multivariate logistic regression analysis, and the significant variables were analyzed by receiver operating characteristic (ROC) analysis. RESULTS: A total of 50 patients were included in this study, with 20 patients in the brain injury group and 30 patients in the normal group. Cardiopulmonary bypass time (min) (107 ± 29 vs. 90 ± 28, P = 0.047) and aortic occlusion time (min) (111 (IQR 81-127) vs. 87 (IQR 72-116), P = 0.010) were significantly longer in the brain injury group than in the normal group. Patients in the brain injury group had greater decreased rSO2 (%) (27.0 ± 7.3 vs. 17.5 ± 6.1, P < 0.001) and cerebral blood flow (%) (44.9 (IQR 37.8-69.2) vs. 29.1 (IQR 12.0-48.2), P = 0.004) levels. Multivariate logistic regression analysis suggested that decreased rSO2 and cerebral blood flow levels, aortic occlusion time, and history of atrial fibrillation were independent risk factors for early postoperative brain injury (P < 0.05). ROC analysis reported that the best cutoff values for predicting early postoperative brain injury were 21.4% and 37.4% for decreased rSO2 and cerebral blood flow levels, respectively (P < 0.05). CONCLUSION: The decreased rSO2 and cerebral blood flow levels, aorta occlusion time, and history of atrial fibrillation were independent risk factors for early postoperative brain injury. TCCD and rSO2 could effectively monitor brain metabolism and cerebral blood flow and predict early postoperative brain injury.

Bayesian estimation and testing in random-effects meta-analysis of rare binary events allowing for flexible group variability.

Rare binary events data arise frequently in medical research. Due to lack of statistical power in individual studies involving such data, meta-analysis has become an increasingly important tool for combining results from multiple independent studies. However, traditional meta-analysis methods often report severely biased estimates in such rare-event settings. Moreover, many rely on models assuming a pre-specified direction for variability between control and treatment groups for mathematical convenience, which may be violated in practice. Based on a flexible random-effects model that removes the assumption about the direction, we propose new Bayesian procedures for estimating and testing the overall treatment effect and inter-study heterogeneity. Our Markov chain Monte Carlo algorithm employs Pólya-Gamma augmentation so that all conditionals are known distributions, greatly facilitating computational efficiency. Our simulation shows that the proposed approach generally reports less biased and more stable estimates compared to existing methods. We further illustrate our approach using two real examples, one using rosiglitazone data from 56 studies and the other using stomach ulcers data from 41 studies.

The Abundance and Function of Neutrophils in the Endometriosis Systemic and Pelvic Microenvironment.

Endometriosis is a common inflammatory illness in which endometrial tissue grows outside the uterine cavity. Immune dysfunction is now widely acknowledged as the primary cause of endometriosis. The immune cell population represented by neutrophils is thought to play an essential role in the etiology, pathophysiology, and associated clinical outcome. There is growing evidence that neutrophils have a role in chronic and aseptic inflammatory diseases, and endometriosis patients have increased levels of neutrophils in plasma, peritoneal fluid, and ectopic endometrium. Here, we sought to review the function of neutrophils in the pathogenesis of endometriosis, with an emphasis on the role of neutrophils in regulating endometrial angiogenesis and the local inflammatory microenvironment.

PLGA-Based Micro/Nanoparticles: An Overview of Their Applications in Respiratory Diseases.

Respiratory diseases, such as asthma and chronic obstructive pulmonary disease (COPD), are critical areas of medical research, as millions of people are affected worldwide. In fact, more than 9 million deaths worldwide were associated with respiratory diseases in 2016, equivalent to 15% of global deaths, and the prevalence is increasing every year as the population ages. Due to inadequate treatment options, the treatments for many respiratory diseases are limited to relieving symptoms rather than curing the disease. Therefore, new therapeutic strategies for respiratory diseases are urgently needed. Poly (lactic-co-glycolic acid) micro/nanoparticles (PLGA M/NPs) have good biocompatibility, biodegradability and unique physical and chemical properties, making them one of the most popular and effective drug delivery polymers. In this review, we summarized the synthesis and modification methods of PLGA M/NPs and their applications in the treatment of respiratory diseases (asthma, COPD, cystic fibrosis (CF), etc.) and also discussed the research progress and current research status of PLGA M/NPs in respiratory diseases. It was concluded that PLGA M/NPs are the promising drug delivery vehicles for the treatment of respiratory diseases due to their advantages of low toxicity, high bioavailability, high drug loading capacity, plasticity and modifiability. And at the end, we presented an outlook on future research directions, aiming to provide some new ideas for future research directions and hopefully to promote their widespread application in clinical treatment.

Azo-Linkage Redox Metal-Organic Framework Incorporating Carbon Nanotubes for High-Performance Aqueous Energy Storage.

The design of well-defined hierarchical free-standing electrodes for robust high-performance energy storage is challenging. We report herein that azo-linkage redox metal-organic frameworks (MOFs) incorporate single-walled carbon nanotubes (CNTs) as flexible electrodes. The in situ-guided growth, crystallinity and morphology of UiO-66-NO2 MOFs were finely controlled in the presence of CNTs. The MOFs' covalent anchoring to CNTs and solvothermal grafting anthraquinone (AQ) pendants endow the hybrid (denoted as CNT@UiO-66-AQ) with greatly improved conductivity, charge storage pathways and electrochemical dynamics. The flexible CNT@UiO-66-AQ displays a highest areal specific capacitance of 302.3 mF cm-2 (at 1 mA cm-2) in -0.4~0.9 V potential window, together with 100% capacitance retention over 5000 cycles at 5 mA cm-2. Its assembled symmetrical supercapacitor (SSC) achieves a maximum energy density of 0.037 mWh cm-2 and a maximum power density of 10.4 mW cm-2, outperforming many MOFs-hybrids-based SSCs in the literature. Our work may open a new avenue for preparing azo-coupled redox MOFs hybrids with carbaneous substrates for high-performance robust aqueous energy storage.

Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences.

Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology. However, the one-to-many correspondence between a patient and multiple TCR sequences hinders researchers from simply adopting classical statistical/machine learning methods. There were recent attempts to model this type of data in the context of multiple instance learning (MIL). Despite the novel application of MIL to cancer detection using TCR sequences and the demonstrated adequate performance in several tumor types, there is still room for improvement, especially for certain cancer types. Furthermore, explainable neural network models are not fully investigated for this application. In this article, we propose multiple instance neural networks based on sparse attention (MINN-SA) to enhance the performance in cancer detection and explainability. The sparse attention structure drops out uninformative instances in each bag, achieving both interpretability and better predictive performance in combination with the skip connection. Our experiments show that MINN-SA yields the highest area under the ROC curve scores on average measured across 10 different types of cancers, compared to existing MIL approaches. Moreover, we observe from the estimated attentions that MINN-SA can identify the TCRs that are specific for tumor antigens in the same T cell repertoire.

RCRdiff: A fully integrated Bayesian method for differential expression analysis using raw NanoString nCounter data.

The medium-throughput mRNA abundance platform NanoString nCounter has gained great popularity in the past decade, due to its high sensitivity and technical reproducibility as well as remarkable applicability to ubiquitous formalin fixed paraffin embedded (FFPE) tissue samples. Based on RCRnorm developed for normalizing NanoString nCounter data and Bayesian LASSO for variable selection, we propose a fully integrated Bayesian method, called RCRdiff, to detect differentially expressed (DE) genes between different groups of tissue samples (eg, normal and cancer). Unlike existing methods that often require normalization performed beforehand, RCRdiff directly handles raw read counts and jointly models the behaviors of different types of internal controls along with DE and non-DE gene patterns. Doing so would avoid efficiency loss caused by ignoring estimation uncertainty from the normalization step in a sequential approach and thus can offer more reliable statistical inference. We also propose clustering-based strategies for DE gene selection, which do not require any external dataset and are free of any arbitrary cutoff. Empirical evidence of the attractiveness of RCRdiff is demonstrated via extensive simulation and data examples.

Effect of Doppler shift on preamplifier DPSK receivers using balanced detection for optical satellite networks.

Optical satellite networks using laser intersatellite links are recognized as being capable of satisfying the increasing broadband communication demands. However, the Doppler shift due to relative movement between satellites must be taken into consideration. A convenient method that does not require complicated mathematical calculation is adopted to derive the relationship between the residual Doppler shift and the bit error rate (BER) for the balanced differential phase-shift keying (DPSK) detection system. It is shown that the DPSK receiver is sensitive to the residual Doppler shift; when the residual Doppler shift Δfres/Rb is 0.05, 0.10, and 0.15, the BER is reduced from a level of 10-17 to a level of 10-14, 10-11, and 10-6, respectively. The BER of a certain node pair under different link assignment schemes is calculated in a typical multi-hop LEO constellation. The results show that the communication BER of the same node pair is not only related to the step change of the Doppler but also related to the accumulation of the Doppler shift in the optical path. The results obtained here will be helpful in routing selection, link assignment, topology design, and system compensation.

Empirical likelihood inference for area under the receiver operating characteristic curve using ranked set samples.

The area under a receiver operating characteristic curve (AUC) is a useful tool to assess the performance of continuous-scale diagnostic tests on binary classification. In this article, we propose an empirical likelihood (EL) method to construct confidence intervals for the AUC from data collected by ranked set sampling (RSS). The proposed EL-based method enables inferences without assumptions required in existing nonparametric methods and takes advantage of the sampling efficiency of RSS. We show that for both balanced and unbalanced RSS, the EL-based point estimate is the Mann-Whitney statistic, and confidence intervals can be obtained from a scaled chi-square distribution. Simulation studies and two case studies on diabetes and chronic kidney disease data suggest that using the proposed method and RSS enables more efficient inference on the AUC.

Recent Advances in Nanomaterials for Asthma Treatment.

Asthma is a chronic airway inflammatory disease with complex mechanisms, and these patients often encounter difficulties in their treatment course due to the heterogeneity of the disease. Currently, clinical treatments for asthma are mainly based on glucocorticoid-based combination drug therapy; however, glucocorticoid resistance and multiple side effects, as well as the occurrence of poor drug delivery, require the development of more promising treatments. Nanotechnology is an emerging technology that has been extensively researched in the medical field. Several studies have shown that drug delivery systems could significantly improve the targeting, reduce toxicity and improve the bioavailability of drugs. The use of multiple nanoparticle delivery strategies could improve the therapeutic efficacy of drugs compared to traditional delivery methods. Herein, the authors presented the mechanisms of asthma development and current therapeutic methods. Furthermore, the design and synthesis of different types of nanomaterials and micromaterials for asthma therapy are reviewed, including polymetric nanomaterials, solid lipid nanomaterials, cell membranes-based nanomaterials, and metal nanomaterials. Finally, the challenges and future perspectives of these nanomaterials are discussed to provide guidance for further research directions and hopefully promote the clinical application of nanotherapeutics in asthma treatment.

Effects of phytase and 25-hydroxyvitamin D3 supplementation on growth performance and bone development in weaned piglets in Ca- and P-deficient dietary.

BACKGROUND: The beneficial function of phytase and 25-hydroxyvitamin D3 (HyD) on the feed utilization rate has been widely investigated. However, studies concerning its influence on weaned piglets largely lag behind. The aim of this study was to investigate the effects of phytase and HyD supplementation on the growth performance and bone development in weaned piglets under dietary Ca and P deficiency. RESULTS: The results showed that dietary Ca and P deficiency decreased (P < 0.05) the content of serum P in 6-10 kg piglets, as well as reducing (P < 0.05) the contents of serum Ca and P, average daily gain (ADG), bone mineral density (BMD), breaking force (BF), bone ash and femur Ca in 10-20 kg piglets. Compared with the control group, the feed-to-gain ratio (F/G) of 6-10 kg piglets in the Phy group was decreased (P < 0.05), whereas the ADG, blood Ca and P, BMD, BF, bone ash, P apparent digestibility, Ca and P retention rate of 10-20 kg piglets were increased (P < 0.05). The contents of serum osteocalcin and HyD in 6-10 kg piglets and ADG were higher than in the control group (P < 0.05), as well as the contents of serum Ca and HyD in 10-20 kg piglets in the HyD treatment group. Supplementation with both Phy and HyD decreased the F/D (P < 0.05) and increased the contents of serum Ca, P and HyD in 6-10 kg piglets as well as enhancing the ADG, BMD, BF, bone ash, femur Ca and P, serum Ca and P, HyD, and the apparent digestibility and retention of Ca and P (P < 0.05) in 10-20 kg piglets. Supplementation with Phy and HyD in Ca- and P-deficient dietary decreased bone resorption, and improved tight arrangement of collagen fibers and oblique fibers in weaned piglets. CONCLUSION: These data indicated that supplementation with both 1500 U kg-1 Phy and 50 µg kg-1 HyD could enhance dietary Ca and P utilization and promote bone development in low Ca and P dietary, and supplementation with both Phy and HyD had a significant synergy effect compared to single supplement. © 2021 Society of Chemical Industry.

The effect of methanol fixation on single-cell RNA sequencing data.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has led to remarkable progress in our understanding of tissue heterogeneity in health and disease. Recently, the need for scRNA-seq sample fixation has emerged in many scenarios, such as when samples need long-term transportation, or when experiments need to be temporally synchronized. Methanol fixation is a simple and gentle method that has been routinely applied in scRNA-sEq. Yet, concerns remain that fixation may result in biases which may change the RNA-seq outcome. RESULTS: We adapted an existing methanol fixation protocol and performed scRNA-seq on both live and methanol fixed cells. Analyses of the results show methanol fixation can faithfully preserve biological related signals, while the discrepancy caused by fixation is subtle and relevant to library construction methods. By grouping transcripts based on their lengths and GC content, we find that transcripts with different features are affected by fixation to different degrees in full-length sequencing data, while the effect is alleviated in Drop-seq result. CONCLUSIONS: Our deep analysis reveals the effects of methanol fixation on sample RNA integrity and elucidates the potential consequences of using fixation in various scRNA-seq experiment designs.