Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry.

Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.

Advanced sequencing-based high-throughput and long-read single-cell transcriptome analysis.

Cells are the fundamental building blocks of living systems, exhibiting significant heterogeneity. The transcriptome connects the cellular genotype and phenotype, and profiling single-cell transcriptomes is critical for uncovering distinct cell types, states, and the interplay between cells in development, health, and disease. Nevertheless, single-cell transcriptome analysis faces daunting challenges due to the low abundance and diverse nature of RNAs in individual cells, as well as their heterogeneous expression. The advent and continuous advancements of next-generation sequencing (NGS) and third-generation sequencing (TGS) technologies have solved these problems and facilitated the high-throughput, sensitive, full-length, and rapid profiling of single-cell RNAs. In this review, we provide a broad introduction to current methodologies for single-cell transcriptome sequencing. First, state-of-the-art advancements in high-throughput and full-length single-cell RNA sequencing (scRNA-seq) platforms using NGS are reviewed. Next, TGS-based long-read scRNA-seq methods are summarized. Finally, a brief conclusion and perspectives for comprehensive single-cell transcriptome analysis are discussed.

Well-Paired-Seq2: High-Throughput and High-Sensitivity Strategy for Characterizing Low RNA-Content Cell/Nucleus Transcriptomes.

Single-cell RNA sequencing (scRNA-seq) is a transformative technology that unravels the intricate cellular state heterogeneity. However, the Poisson-dependent cell capture and low sensitivity in scRNA-seq methods pose challenges for throughput and samples with a low RNA-content. Herein, to address these challenges, we present Well-Paired-Seq2 (WPS2), harnessing size-exclusion and quasi-static hydrodynamics for efficient cell capture. WPS2 exploits molecular crowding effect, tailing activity enhancement in reverse transcription, and homogeneous enzymatic reaction in the initial bead-based amplification to achieve 3116 genes and 8447 transcripts with an average of ∼20000 reads per cell. WPS2 detected 1420 more genes and 4864 more transcripts than our previous Well-Paired-Seq. It sensitively characterizes transcriptomes of low RNA-content single cells and nuclei, overcoming the Poisson limit for cell and barcoded bead capture. WPS2 also profiles transcriptomes from frozen clinical samples, revealing heterogeneous tumor copy number variations and intercellular crosstalk in clear cell renal cell carcinomas. Additionally, we provide the first single-cell-level characterization of rare metanephric adenoma (MA) and uncover potential specific markers. With the advantages of high sensitivity and high throughput, WPS2 holds promise for diverse basic and clinical research.

Population pharmacokinetic approach to guide personalized sertraline treatment in Chinese patients.

Object: Sertraline is a first-line SSRI for the treatment of depression and has the same effectiveness along with a superior safety profile compared to other medications. There are few population pharmacokinetic (PPK) studies of sertraline and a lack of studies in the Chinese population. Therefore, we performed a PPK analysis of Chinese patients treated with sertraline to identify factors that can influence drug exposure. In addition, the dosing and discontinuation regimen of sertraline when applied to adolescents was explored. Methods: Sertraline serum drug concentration data were collected from 140 hospitalized patients to generate a sertraline PPK dataset, and data evaluation and examination of the effects of covariates on drug exposure in the final model were performed using nonlinear mixed-effects models (NONMEM) and first-order conditional estimation with interaction (FOCE-I). Examining rational medication administration and rational withdrawal of sertraline based on significant covariates and final modeling. Results: A one-compartment model with first-order absorption and elimination of sertraline was developed for Chinese patients with psychiatric disorders. Analysis of covariates revealed that age was a covariate that significantly affected sertraline CL/F (P < 0.01) and that sertraline clearance decreased progressively with aging, whereas other factors had no effect on CL/F and V/F of sertraline. In the age range of 11-79, there were 54 adolescent patients (about 1/3) aged 13-18 years, and the safe and effective optimal daily dose for adolescent patients based on the final model simulations was 50-250 mg/d. For adolescent patients, serum concentration fluctuations were moderate for OD doses of 50 mg and 100 mg, using a fixed dose-descent regimen. For patients with OD doses of 150-200 mg and BID doses of 100-200 mg, a more gradual decrease in serum concentration was achieved with a fixed dose interval of 7 or 14 days for 25 mg as the regimen of descent. Conclusions: To our knowledge, this may be the first PPK study of sertraline in Chinese patients. We found that age was an important factor affecting clearance in Chinese patients taking sertraline. Patients taking sertraline may be exposed to increased amounts of sertraline due to decreased clearance with increasing age. The rational dosing and safe discontinuation of sertraline in adolescent patients can be appropriately referenced to the results of the model simulation, thus providing assistance for individualized dosing in adolescents.

A Bibliometric and Visual Analysis of Single Nucleotide Polymorphism Studies in Depression.

BACKGROUND: Genetic polymorphism has been proven to have an important association with depression, which can influence the risk of developing depression, the efficacy of medications, and adverse effects via metabolic and neurological pathways. Nonetheless, aspects of the association between single nucleotide polymorphisms and depression have not been systematically investigated by bibliometric analysis. OBJECTIVE: The aim of this study was to analyze the current status and trends of single nucleotide polymorphism research on depression through bibliometric and visual analysis. METHODS: The Web of Science Core Collection was used to retrieve 10,043 articles that were published between 1998 and 2021. CiteSpace (6.1 R4) was used to perform collaborative network analysis, co-citation analysis, co-occurrence analysis, and citation burst detection. RESULTS: The most productive and co-cited journals were the Journal of Affective Disorders and Biological Psychiatry, respectively, and an analysis of the references showed that the most recent research focused on the largest thematic cluster, "5-HT", reflecting the important research base in this area. "CYP2D6" has been in the spotlight since its emergence in 2009 and has become a research hotspot since its outbreak in 2019. However, "BDNF ", "COMT ", "older adults", "loci", and "DNA methylation" are also the new frontier of research, and some of them are currently in the process of exploration. CONCLUSION: These findings offer a useful perspective on existing research and potential future approaches in the study of the association between single nucleotide polymorphisms and depression, which may assist researchers in selecting appropriate collaborators or journals.

Factors influencing concentrations of risperidone and 9-hydroxyrisperidone in psychiatric outpatients taking immediate-release formulations of risperidone.

OBJECTIVES: To analyze the factors affecting the concentrations of the active moiety of risperidone (RIS) and its active metabolite 9-hydroxyrisperidone (9-OH-RIS) in psychiatric outpatients taking immediate-release formulations. METHODS: This is a retrospective study on the therapeutic drug monitoring (TDM) data regarding RIS and 9-OH-RIS in adult psychiatric outpatients. TDM data with simultaneous RIS and 9-OH-RIS monitoring from March 2018 to February 2020 and relevant medical records (including dosage, dosage form, sex, age, diagnosis, combined medication, and comorbid disease) from 399 adult psychiatric outpatients (223 males and 176 females) were included in this study. RESULTS: The daily dose of RIS was 5.56 ± 2.05 mg, the concentration of total active moiety was 42.35 ± 25.46 ng/mL, and the dose-adjusted plasma concentration (C/D) of active moiety was 7.83 ± 3.87 (ng/ml)/(mg/day). Dose, sex, and age were identified as important factors influencing concentrations of RIS and 9-OH-RIS in adult psychiatric outpatients. CONCLUSIONS: Individualized medication adjustments should be made according to the specific conditions of psychiatric outpatients. The findings strongly support the use of TDM to guide dosing decisions in psychiatric outpatients taking RIS.

Bilateral theta burst stimulation for patients with acute unipolar or bipolar depressive episodes: A systematic review of randomized controlled studies.

OBJECTIVE: This meta-analysis of randomized controlled trials (RCTs) evaluated the overall efficacy and safety of bilateral theta-burst stimulation (TBS) as an intervention for patients with mood disorders. METHODS: A systematic search (up to December 7, 2022) of RCTs was conducted to address the study aims. A random-effects meta-analysis was performed by including study-defined responses and remission as primary outcomes. RESULTS: Analyses included six RCTs comprising 285 participants with major depressive disorder (MDD) (n = 233) or a depressive episode in the course of bipolar disorder (BD) (n = 52) who had undergone active bilateral TBS (n = 142) versus sham stimulation (n = 143). Active bilateral TBS outperformed sham stimulation with respect to study-defined improvements (55.1 % versus 20.3 %, 4 RCTs, n = 152, 95%CI: 1.63 to 4.39, P < 0.0001; I2 = 0 %) and remission rates (37.2 % versus 14.3 %, 2 RCTs, n = 85, 95%CI: 1.13 to 5.95, P = 0.02; I2 = 0 %) in MDD patients but not those with bipolar or unipolar mixed depression. Superiority of active bilateral TBS over sham stimulation was confirmed for improvements in depressive symptoms at post-bilateral TBS assessments and 8-week follow-ups in patients with either MDD or mixed depression (all P < 0.05). Discontinuation rates due to any reason and adverse events (i.e., headache, dizziness) were similar between TBS and sham stimulation groups with MDD or mixed depression (all P > 0.05). CONCLUSION: Bilateral TBS targeting the dorsolateral prefrontal cortex (DLPFC) appears to be a well-tolerated form of repetitive transcranial magnetic stimulation (rTMS) that has substantial antidepressant effects, particularly in patients with MDD. Effects of bilateral TBS on bipolar and unipolar mixed depression should be further investigated.

Kynurenine metabolite changes in individuals with alcohol use disorder: A systematic review and meta-analysis.

OBJECTIVE: Growing evidence suggests an abnormal metabolism of kynurenine in individuals with alcohol use disorder (AUD). This systematic review and meta-analysis was aimed at assessing the possible differences in kynurenine metabolites between individuals with AUD and controls. METHODS: We searched PubMed, Embase, and Web of Science databases and included any clinical studies comparing the peripheral blood levels of at least one metabolite, between individuals with AUD and controls without AUD. Random-effects meta-analyses were conducted to generate pooled standardized mean differences (SMD). Subgroup analyses and meta-regression analyses were conducted. RESULTS: A total of seven eligible studies with 572 participants were included. The peripheral blood levels of kynurenine (SMD = 0.58; p = 0.004) along with the ratio of kynurenine and tryptophan (SMD = 0.73; p = 0.002) were higher in individuals with AUD, while kynurenic acid levels (SMD = -0.81; p = 0.003) were reduced in individuals with AUD compared to controls. The peripheral blood levels of tryptophan along with the ratio of kynurenic acid and kynurenine were unaltered. Subgroup analyses confirmed these results. CONCLUSION: Our results suggested a shift in the tryptophan metabolism to the kynurenine pathway and a down-regulation of the potentially neuroprotective kynurenic acid in individuals with AUD.

Development and validation of an LC-MS/MS method by one-step precipitation for cinacalcet in human plasma.

A sensitive, convenient, rapid and economic liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was developed to determine cinacalcet concentration in human plasma. A stable isotope cinacalcet (cinacalcet-D3) was selected as internal standard and the analytes were extracted from plasma samples by a one-step precipitation procedure. Chromatography separation was conducted on an Eclipse Plus C18 column by gradient elution with mobile phase of methanol-water-ammonium formate system at a constant flow rate of 0.6 mL/min. Mass spectrometric detection was conducted by multiple reaction monitoring using positive electrospray ionization. Cinacalcet concentrations in human plasma were determined over the concentration range of 0.1-50 ng/mL. The accuracies of lower limit of quantification (LLOQ) and quality control samples were all within the range of 85-115%, and the inter- and intra-batch precisions (CV%) were all within 15%. The average extraction recovery rates were 95.67-102.88%, and the quantification was not interfered by the matrix components. The validated method was successfully applied to determined cinacalcet concentrations in human plasma from secondary hyperparathyroidism patients.

System comprehensive risk assessment of urban rainstorm-induced flood-water pollution disasters.

The urban rainstorm-induced flood-water pollution disaster is a kind of systematic risk, which may induce secondary disasters that can lead to more serious damage, so this paper first adopts the fuzzy comprehensive evaluation method to determine the flood risk by combining with the submergence depth derived from the risk field and other factors data, and then the grid environmental risk evaluation method, which is improved by increasing the induced possibility based on Bayesian theory, is used to evaluate the flood-induced water pollution risk, and the system comprehensive risk of rainstorm-induced flood-water pollution disasters is finally obtained by constructing risk level matrix, which can well depict the coupling superposition effect. Shenzhen City is selected as the study area, and the results showed that the area with high-risk of both flood and water pollution only accounts for about 0.14% of the total area, mainly distributed in the eastern junction of Longgang district and Pingshan district, where the rainstorms occur frequently and the enterprise risk sources are dense. The system comprehensive risk is mostly very low-low and very high-low, accounting for more than 76% of the total area. It is always necessary to pay attention not only to the areas with high risk level of both disasters, but also to the areas with high risk level of one disaster. The method proposed in this study can not only quantitatively reveal the formation of the induced risk, but also provide reference for early warning.

Joint population pharmacokinetic modeling of venlafaxine and O-desmethyl venlafaxine in healthy volunteers and patients to evaluate the impact of morbidity and concomitant medication.

Introduction: Venlafaxine (VEN) is a widely used dual selective serotonin/noradrenaline reuptake inhibitor indicated for depression and anxiety. It undergoes first-pass metabolism to its active metabolite, O-desmethyl venlafaxine (ODV). The aim of the present study was to develop a joint population pharmacokinetic (PPK) model to characterize their pharmacokinetic characters simultaneously. Methods: Plasma concentrations with demographic and clinical data were derived from a bioequivalence study in 24 healthy subjects and a naturalistic TDM setting containing 127 psychiatric patients. A parent-metabolite PPK modeling was performed with NONMEM software using a non-linear mixed effect modeling approach. Goodness of fit plots and normalized prediction distribution error method were used for model validation. Results and conclusion: Concentrations of VEN and ODV were well described with a one-compartment model incorporating first-pass metabolism. The first-pass metabolism was modeled as a first-order conversion. The morbid state and concomitant amisulpride were identified as two significant covariates affecting the clearance of VEN and ODV, which may account for some of the variations in exposure. This model may contribute to the precision medication in clinical practice and may inspire other drugs with pre-system metabolism.

An interpretable stacking ensemble learning framework based on multi-dimensional data for real-time prediction of drug concentration: The example of olanzapine.

Background and Aim: Therapeutic drug monitoring (TDM) has evolved over the years as an important tool for personalized medicine. Nevertheless, some limitations are associated with traditional TDM. Emerging data-driven model forecasting [e.g., through machine learning (ML)-based approaches] has been used for individualized therapy. This study proposes an interpretable stacking-based ML framework to predict concentrations in real time after olanzapine (OLZ) treatment. Methods: The TDM-OLZ dataset, consisting of 2,142 OLZ measurements and 472 features, was formed by collecting electronic health records during the TDM of 927 patients who had received OLZ treatment. We compared the performance of ML algorithms by using 10-fold cross-validation and the mean absolute error (MAE). The optimal subset of features was analyzed by a random forest-based sequential forward feature selection method in the context of the top five heterogeneous regressors as base models to develop a stacked ensemble regressor, which was then optimized via the grid search method. Its predictions were explained by using local interpretable model-agnostic explanations (LIME) and partial dependence plots (PDPs). Results: A state-of-the-art stacking ensemble learning framework that integrates optimized extra trees, XGBoost, random forest, bagging, and gradient-boosting regressors was developed for nine selected features [i.e., daily dose (OLZ), gender_male, age, valproic acid_yes, ALT, K, BW, MONO#, and time of blood sampling after first administration]. It outperformed other base regressors that were considered, with an MAE of 0.064, R-square value of 0.5355, mean squared error of 0.0089, mean relative error of 13%, and ideal rate (the percentages of predicted TDM within ± 30% of actual TDM) of 63.40%. Predictions at the individual level were illustrated by LIME plots, whereas the global interpretation of associations between features and outcomes was illustrated by PDPs. Conclusion: This study highlights the feasibility of the real-time estimation of drug concentrations by using stacking-based ML strategies without losing interpretability, thus facilitating model-informed precision dosing.

Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients.

Background and aim: Available evidence suggests elevated serum prolactin (PRL) levels in olanzapine (OLZ)-treated patients with schizophrenia. However, machine learning (ML)-based comprehensive evaluations of the influence of pathophysiological and pharmacological factors on PRL levels in OLZ-treated patients are rare. We aimed to forecast the PRL level in OLZ-treated patients and mine pharmacovigilance information on PRL-related adverse events by integrating ML and electronic health record (EHR) data. Methods: Data were extracted from an EHR system to construct an ML dataset in 672×384 matrix format after preprocessing, which was subsequently randomly divided into a derivation cohort for model development and a validation cohort for model validation (8:2). The eXtreme gradient boosting (XGBoost) algorithm was used to build the ML models, the importance of the features and predictive behaviors of which were illustrated by SHapley Additive exPlanations (SHAP)-based analyses. The sequential forward feature selection approach was used to generate the optimal feature subset. The co-administered drugs that might have influenced PRL levels during OLZ treatment as identified by SHAP analyses were then compared with evidence from disproportionality analyses by using OpenVigil FDA. Results: The 15 features that made the greatest contributions, as ranked by the mean (|SHAP value|), were identified as the optimal feature subset. The features were gender_male, co-administration of risperidone, age, co-administration of aripiprazole, concentration of aripiprazole, concentration of OLZ, progesterone, co-administration of sulpiride, creatine kinase, serum sodium, serum phosphorus, testosterone, platelet distribution width, α-L-fucosidase, and lipoprotein (a). The XGBoost model after feature selection delivered good performance on the validation cohort with a mean absolute error of 0.046, mean squared error of 0.0036, root-mean-squared error of 0.060, and mean relative error of 11%. Risperidone and aripiprazole exhibited the strongest associations with hyperprolactinemia and decreased blood PRL according to the disproportionality analyses, and both were identified as co-administered drugs that influenced PRL levels during OLZ treatment by SHAP analyses. Conclusions: Multiple pathophysiological and pharmacological confounders influence PRL levels associated with effective treatment and PRL-related side-effects in OLZ-treated patients. Our study highlights the feasibility of integration of ML and EHR data to facilitate the detection of PRL levels and pharmacovigilance signals in OLZ-treated patients.

Population pharmacokinetics and dosing optimization of olanzapine in Chinese paediatric patients: Based on the impact of sex and concomitant valproate on clearance.

WHAT IS KNOWN AND OBJECTIVE: Olanzapine is an atypical antipsychotic drug used for mental disorders. There are limited studies providing sufficient pharmacokinetic data, thus the variability of concentrations of olanzapine used in Chinese paediatric patients aged 10 to 17 years remains to be evaluated. METHODS: Therapeutic drug monitoring data were collected from 151 paediatric patients aged 10 to 17 years who received olanzapine. The model was developed with a NONMEM software program. The final model validation and evaluation were assessed by bootstrap, diagnostic scatter plots, and normalized prediction distribution error (NPDE). Regimens of different dosages were simulated to reach the target concentration levels of 20 ng/ml, by using the final model with typical parameters. RESULTS: The one-compartment model was considered the best fit for the data. Typical estimates of the absorption rate constant (Ka), apparent clearance (CL/F), and apparent distribution volume (V/F) in the final model were 0.142 h-1 , 15.4 L/h, and 322 L, respectively. Sex and concomitant valproate (VPA) were included as significant predictors of olanzapine clearance, which was described by the following equation: CL/F = 15.4 × (1 + 0.546 × SEX) × (1 + 0.264 × VPA). Results of Monte-Carlo simulation suggested that male paediatric patients with concomitant VPA were advised to take no less than 15 mg per day of olanzapine orally, and in female paediatric patients with concomitant VPA, a dosing regimen of 10 mg may be sufficient to achieve the therapeutic range of olanzapine. WHAT IS NEW AND CONCLUSION: Our results identified concomitant valproate and sex as significant covariates in olanzapine population pharmacokinetics. Our model may be a useful tool for recommending dosage adjustments for physicians. The pharmacokinetics of olanzapine in patients aged 10 to 17 years was generally similar to that of adults and the elderly.

Discovery of a Unique Structural Motif in Lanthipeptide Synthetases for Substrate Binding and Interdomain Interactions.

Class III lanthipeptide synthetases catalyze the formation of lanthionine/methyllanthionine and labionin crosslinks. We present here the 2.40 Šresolution structure of the kinase domain of a class III lanthipeptide synthetase CurKC from the biosynthesis of curvopeptin. A unique structural subunit for leader binding, named leader recognition domain (LRD), was identified. The LRD of CurKC is responsible for the recognition of the leader peptide and for mediating interactions between the lyase and kinase domains. LRDs are highly conserved among the kinase domains of class III and class IV lanthipeptide synthetases. The discovery of LRDs provides insight into the substrate recognition and domain organization in multidomain lanthipeptide synthetases.

Pharmacokinetics of immediate and sustained-release formulations of paroxetine: Population pharmacokinetic approach to guide paroxetine personalized therapy in chinese psychotic patients.

Paroxetine is one of the most potent selective serotonin reuptake inhibitors (SSRIs) approved for treating depression, panic disorder, and obsessive-compulsive disorder. There is evidence linking genetic polymorphisms and nonlinear metabolism to the Paroxetine's pharmacokinetic (PK) variability. The purpose of the present study was to develop a population PK (PPK) model of paroxetine in Chinese patients, which was used to define the paroxetine's PK parameters and quantify the effect of clinical and baseline demographic factors on these PK characteristics. The study included 184 inpatients with psychosis (103 females and 81 males), with a total of 372 serum concentrations of paroxetine for PPK analyses. The total daily dosage ranged from 20 to 75 mg. One compartment model could fit the PKs characterize of paroxetine. Covariate analysis revealed that dose, formulation, and sex had a significant effect on the PK parameters of paroxetine; however, there was no evident genetic influence of CYP2D6 enzymes on paroxetine concentrations in Chinese patients. The study determined that the population's apparent distribution volume (V/F) and apparent clearance (CL/F), respectively, were 8850 and 21.2 L/h. The CL/F decreased 1-2-fold for each 10 mg dose increase, whereas the different formulations caused a decrease in V/F of 66.6%. Sex was found to affect bioavailability (F), which decreased F by 47.5%. Females had higher F values than males. This PPK model described data from patients with psychosis who received paroxetine immediate-release tablets (IR-T) and/or sustained-release tablets (SR-T). Paroxetine trough concentrations and relative bioavailability were different between formulations and sex. The altered serum concentrations of paroxetine resulting from individual variants and additive effects need to be considered, to optimize the dosage regimen for individual patients.

Population pharmacokinetics model for escitalopram in Chinese psychiatric patients: effect of CYP2C19 and age.

Objective: To establish a population pharmacokinetic model in Chinese psychiatric patients to characterize escitalopram pharmacokinetic profile to identify factors influencing drug exposure, and through simulation to compare the results with the established therapeutic reference range. Methods: Demographic information, dosing regimen, CYP2C19 genotype, concomitant medications, and liver and kidney function indicators were retrospectively collected for inpatients taking escitalopram with therapeutic drug monitoring from 2018 to 2021. Nonlinear mixed-effects modeling was used to model the pharmacokinetic characteristics of escitalopram. Goodness-of-fit plots, bootstrapping, and normalized prediction distribution errors were used to evaluate the model. Simulation for different dosing regimens was based on the final estimations. Results: The study comprised 106 patients and 337 measurements of serum sample. A structural model with one compartment with first-order absorption and elimination described the data adequately. The population-estimated apparent volume of distribution and apparent clearance were 815 and 16.3 L/h, respectively. Age and CYP2C19 phenotype had a significant effect on the apparent clearance (CL/F). CL/F of escitalopram decreased with increased age, and CL/F of poor metabolizer patients was significantly lower than in extensive and immediate metabolizer patients. The final model-based simulation showed that the daily dose of adolescents with poor metabolizer might be as high as 15 mg or 20 mg and referring to the therapeutic range for adults may result in overdose and a high risk of adverse effects in older patients. Conclusion: A population pharmacokinetics model of escitalopram was successfully created for the Chinese population. Depending on the age of the patients, CYP2C19 genotype and serum drug concentrations throughout treatment are required for adequate individualization of dosing regimens. When developing a regimen for older patients, especially those who are poor metabolizers, vigilance is required.

Directed evolution of the genetically encoded zinc(II) FRET sensor ZapCY1.

Zinc(II) ions (Zn2+) play an essential role in living systems, with their delicate concentration balance differing among the various intracellular organelles. The spatiotemporal distribution and homeostasis of Zn2+ can be monitored through photoluminescence imaging using zinc sensors. Among such biosensors, genetically encoded fluorescent sensor proteins are attractive tools owing to their subcellular localization advantage and high biocompatibility. However, the limited fluorescent properties of these proteins, such as their insufficient quantum yield and dynamic range, restrict their practical use. In this study, we developed an expression-screening-directed evolution system and used it to improve ZapCY1, a genetically encoded fluorescence resonance energy transfer (FRET) sensor. After four rounds of directed evolution, the FRET dynamic range of the modified sensor (designated ZapTV-EH) was increased by 1.5-1.7-fold. With its enhanced signal-to-noise ratio and ability to detect a wide Zn2+ concentration range, ZapTV-EH proves to be a better visualization tool for monitoring Zn2+ at the subcellular level. Combined with the simplified subcloning and expression steps and sufficient mutant libraries, this directed evolution system may provide a more simple and efficient way to develop and optimize genetically encoded FRET sensors through high-throughput screening.

Quantification of Intracellular Proteins in Single Cells Based on Engineered Picoliter Droplets.

Unlike conventional bulk measurements, single-cell protein analysis permits quantification of protein expression in individual cells. This has shed light on the cell-to-cell variation in heterogeneous biological systems, such as solid tumors, brain tissues, and developing embryos. Herein, a microfluidic method is developed to profile protein expression in individual cells by performing single-cell intracellular protein immunoassay in picoliter paired droplets. The high sensitivity of single-cell protein analysis on a chip is achieved by the confined reaction volume of picoliter droplets, efficient kinetic characteristics of the immunoassay through active mixing, and minimum single-cell protein loss by integrated operations. The abundance of an intracellular prostate specific antigen at the single-cell level is measured, and then the platform is applied to identify cell types and investigate heterogeneity within cell populations. Overall, a paired chip for single-cell immunoassay establishes a foundation for parallel, sensitive, and integrated protein quantification at the single-cell level and will find wide applications in the field of single-cell proteomics.