GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution.

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

Investigation of normalization procedures for transcriptome profiles of compounds oriented toward practical study design.

The transcriptome profile is a representative phenotype-based descriptor of compounds, widely acknowledged for its ability to effectively capture compound effects. However, the presence of batch differences is inevitable. Despite the existence of sophisticated statistical methods, many of them presume a substantial sample size. How should we design a transcriptome analysis to obtain robust compound profiles, particularly in the context of small datasets frequently encountered in practical scenarios? This study addresses this question by investigating the normalization procedures for transcriptome profiles, focusing on the baseline distribution employed in deriving biological responses as profiles. Firstly, we investigated two large GeneChip datasets, comparing the impact of different normalization procedures. Through an evaluation of the similarity between response profiles of biological replicates within each dataset and the similarity between response profiles of the same compound across datasets, we revealed that the baseline distribution defined by all samples within each batch under batch-corrected condition is a good choice for large datasets. Subsequently, we conducted a simulation to explore the influence of the number of control samples on the robustness of response profiles across datasets. The results offer insights into determining the suitable quantity of control samples for diminutive datasets. It is crucial to acknowledge that these conclusions stem from constrained datasets. Nevertheless, we believe that this study enhances our understanding of how to effectively leverage transcriptome profiles of compounds and promotes the accumulation of essential knowledge for the practical application of such profiles.

Clinical symptoms, biochemistry, and liver histology during the native liver period of progressive familial intrahepatic cholestasis type 2.

BACKGROUND: Progressive familial intrahepatic cholestasis type 2 (PFIC2) is an ultra-rare disease caused by mutations in the ABCB11 gene. This study aimed to understand the course of PFIC2 during the native liver period. METHODS: From November 2014 to October 2015, a survey to identify PFIC2 patients was conducted in 207 hospitals registered with the Japanese Society of Pediatric Gastroenterology, Hepatology, and Nutrition. Investigators retrospectively collected clinical data at each facility in November 2018 using pre-specified forms. RESULTS: Based on the biallelic pathogenic variants in ABCB11 and/or no hepatic immunohistochemical detection of BSEP, 14 Japanese PFIC2 patients were enrolled at seven facilities. The median follow-up was 63.2 [47.7-123.3] months. The median age of disease onset was 2.5 [1-4] months. Twelve patients underwent living donor liver transplantation (LDLT), with a median age at LDLT of 9 [4-57] months. Two other patients received sodium 4-phenylbutyrate (NaPB) therapy and survived over 60 months with the native liver. No patients received biliary diversion. The cases that resulted in LDLT had gradually deteriorated growth retardation, biochemical tests, and liver histology since the initial visit. In the other two patients, jaundice, growth retardation, and most of the biochemical tests improved after NaPB therapy was started, but pruritus and liver fibrosis did not. CONCLUSIONS: Japanese PFIC2 patients had gradually worsening clinical findings since the initial visit, resulting in LDLT during infancy. NaPB therapy improved jaundice and growth retardation but was insufficient to treat pruritus and liver fibrosis.

Difficulty in chirality recognition for Transformer architectures learning chemical structures from string representations.

Recent years have seen rapid development of descriptor generation based on representation learning of extremely diverse molecules, especially those that apply natural language processing (NLP) models to SMILES, a literal representation of molecular structure. However, little research has been done on how these models understand chemical structure. To address this black box, we investigated the relationship between the learning progress of SMILES and chemical structure using a representative NLP model, the Transformer. We show that while the Transformer learns partial structures of molecules quickly, it requires extended training to understand overall structures. Consistently, the accuracy of molecular property predictions using descriptors generated from models at different learning steps was similar from the beginning to the end of training. Furthermore, we found that the Transformer requires particularly long training to learn chirality and sometimes stagnates with low performance due to misunderstanding of enantiomers. These findings are expected to deepen the understanding of NLP models in chemistry.

Identification of activity-based biomarkers for early-stage pancreatic tumors in blood using single-molecule enzyme activity screening.

Single-molecule enzyme activity-based enzyme profiling (SEAP) is a methodology to globally analyze protein functions in living samples at the single-molecule level. It has been previously applied to detect functional alterations in phosphatases and glycosidases. Here, we expand the potential for activity-based biomarker discovery by developing a semi-automated synthesis platform for fluorogenic probes that can detect various peptidases and protease activities at the single-molecule level. The peptidase/protease probes were prepared on the basis of a 7-amino-4-methylcoumarin fluorophore. The introduction of a phosphonic acid to the core scaffold made the probe suitable for use in a microdevice-based assay, while phosphonic acid served as the handle for the affinity separation of the probe using Phos-tag. Using this semi-automated scheme, 48 fluorogenic probes for the single-molecule peptidase/protease activity analysis were prepared. Activity-based screening using blood samples revealed altered single-molecule activity profiles of CD13 and DPP4 in blood samples of patients with early-stage pancreatic tumors. The study shows the power of single-molecule enzyme activity screening to discover biomarkers on the basis of the functional alterations of proteins.

Investigation of the usefulness of liver-specific deconvolution method by establishing a liver benchmark dataset.

Immune responses in the liver are related to the development and progression of liver failure, and precise prediction of their behavior is important. Deconvolution is a methodology for estimating the immune cell proportions from the transcriptome, and it is mainly applied to blood-derived samples and tumor tissues. However, the influence of tissue-specific modeling on the estimation results has rarely been investigated. Here, we constructed a system to evaluate the performance of the deconvolution method on liver transcriptome data. We prepared seven mouse liver injury models using small-molecule compounds and established a benchmark dataset with corresponding liver bulk RNA-Seq and immune cell proportions. RNA-Seq expression for nine leukocyte subsets and four liver-associated cell types were obtained from the Gene Expression Omnibus to provide a reference. We found that the combination of reference cell sets affects the estimation results of reference-based deconvolution methods and established a liver-specific deconvolution by optimizing the reference cell set for each cell to be estimated. We applied this model to independent datasets and showed that liver-specific modeling is highly extrapolatable. We expect that this approach will enable sophisticated estimation from rich tissue data accumulated in public databases and to obtain information on aggregated immune cell trafficking.

Thioester-Based Coupled Fluorogenic Assays in Microdevice for the Detection of Single-Molecule Enzyme Activities of Esterases with Specified Substrate Recognition.

Single-molecule enzyme activity assay is a platform that enables the analysis of enzyme activities at single proteoform level. The limitation of the targetable enzymes is the major drawback of the assay, but the general assay platform is reported to study single-molecule enzyme activities of esterases based on the coupled assay using thioesters as substrate analogues. The coupled assay is realized by developing highly water-soluble thiol-reacting probes based on phosphonate-substituted boron dipyrromethene (BODIPY). The system enables the detection of cholinesterase activities in blood samples at single-molecule level, and it is shown that the dissecting alterations of single-molecule esterase activities can serve as an informative platform for activity-based diagnosis.