scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular heterogeneity through high-throughput analysis of individual cells. Nevertheless, challenges arise from prevalent sequencing dropout events and noise effects, impacting subsequent analyses. Here, we introduce a novel algorithm, Single-cell Gene Importance Ranking (scGIR), which utilizes a single-cell gene correlation network to evaluate gene importance. The algorithm transforms single-cell sequencing data into a robust gene correlation network through statistical independence, with correlation edges weighted by gene expression levels. We then constructed a random walk model on the resulting weighted gene correlation network to rank the importance of genes. Our analysis of gene importance using PageRank algorithm across nine authentic scRNA-seq datasets indicates that scGIR can effectively surmount technical noise, enabling the identification of cell types and inference of developmental trajectories. We demonstrated that the edges of gene correlation, weighted by expression, play a critical role in enhancing the algorithm's performance. Our findings emphasize that scGIR outperforms in enhancing the clustering of cell subtypes, reverse identifying differentially expressed marker genes, and uncovering genes with potential differential importance. Overall, we proposed a promising method capable of extracting more information from single-cell RNA sequencing datasets, potentially shedding new lights on cellular processes and disease mechanisms.

SCTC: inference of developmental potential from single-cell transcriptional complexity.

Inferring the developmental potential of single cells from scRNA-Seq data and reconstructing the pseudo-temporal path of cell development are fundamental but challenging tasks in single-cell analysis. Although single-cell transcriptional diversity (SCTD) measured by the number of expressed genes per cell has been widely used as a hallmark of developmental potential, it may lead to incorrect estimation of differentiation states in some cases where gene expression does not decrease monotonously during the development process. In this study, we propose a novel metric called single-cell transcriptional complexity (SCTC), which draws on insights from the economic complexity theory and takes into account the sophisticated structure information of scRNA-Seq count matrix. We show that SCTC characterizes developmental potential more accurately than SCTD, especially in the early stages of development where cells typically have lower diversity but higher complexity than those in the later stages. Based on the SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interplay between cells and genes determines the developmental potential, providing new insights into the understanding of biological development from the perspective of complexity theory.

Predicting metabolite-disease associations based on auto-encoder and non-negative matrix factorization.

Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain metabolites, causing them to either increase or decrease. Detecting these deviations in metabolite levels can aid in diagnosing a disease. Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases. We integrate a variety of similarity networks and then acquire the characteristics of both metabolites and diseases through three specific modules. First, we get the disease characteristics from the five-layer auto-encoder module. Later, in the non-negative matrix factorization module, we extract both the metabolite and disease characteristics. Furthermore, the graph attention auto-encoder module helps us obtain metabolite characteristics. After obtaining the features from three modules, these characteristics are merged into a single, comprehensive feature vector for each metabolite-disease pair. Finally, we send the corresponding feature vector and label to the multi-layer perceptron for training. The experiment demonstrates our area under the receiver operating characteristic curve of 0.975 and area under the precision-recall curve of 0.973 in 5-fold cross-validation, which are superior to those of existing state-of-the-art predictive methods. Through case studies, most of the new associations obtained by MDA-AENMF have been verified, further highlighting the reliability of MDA-AENMF in predicting the potential relationships between metabolites and diseases.

Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods.

Long noncoding RNA (lncRNA) is a kind of noncoding RNA with a length of more than 200 nucleotide units. Numerous research studies have proven that although lncRNAs cannot be directly translated into proteins, lncRNAs still play an important role in human growth processes by interacting with proteins. Since traditional biological experiments often require a lot of time and material costs to explore potential lncRNA-protein interactions (LPI), several computational models have been proposed for this task. In this study, we introduce a novel deep learning method known as combined graph auto-encoders (LPICGAE) to predict potential human LPIs. First, we apply a variational graph auto-encoder to learn the low dimensional representations from the high-dimensional features of lncRNAs and proteins. Then the graph auto-encoder is used to reconstruct the adjacency matrix for inferring potential interactions between lncRNAs and proteins. Finally, we minimize the loss of the two processes alternately to gain the final predicted interaction matrix. The result in 5-fold cross-validation experiments illustrates that our method achieves an average area under receiver operating characteristic curve of 0.974 and an average accuracy of 0.985, which is better than those of existing six state-of-the-art computational methods. We believe that LPICGAE can help researchers to gain more potential relationships between lncRNAs and proteins effectively.

Modeling and analyzing single-cell multimodal data with deep parametric inference.

The proliferation of single-cell multimodal sequencing technologies has enabled us to understand cellular heterogeneity with multiple views, providing novel and actionable biological insights into the disease-driving mechanisms. Here, we propose a comprehensive end-to-end single-cell multimodal analysis framework named Deep Parametric Inference (DPI). DPI transforms single-cell multimodal data into a multimodal parameter space by inferring individual modal parameters. Analysis of cord blood mononuclear cells (CBMC) reveals that the multimodal parameter space can characterize the heterogeneity of cells more comprehensively than individual modalities. Furthermore, comparisons with the state-of-the-art methods on multiple datasets show that DPI has superior performance. Additionally, DPI can reference and query cell types without batch effects. As a result, DPI can successfully analyze the progression of COVID-19 disease in peripheral blood mononuclear cells (PBMC). Notably, we further propose a cell state vector field and analyze the transformation pattern of bone marrow cells (BMC) states. In conclusion, DPI is a powerful single-cell multimodal analysis framework that can provide new biological insights into biomedical researchers. The python packages, datasets and user-friendly manuals of DPI are freely available at https://github.com/studentiz/dpi.

An entropy-based approach for assessing the directional persistence of cell migration.

Cell migration, which is primarily characterized by directional persistence, is essential for the development of normal tissues and organs, as well as for numerous pathological processes. However, there is a lack of simple and efficient tools to analyze the systematic properties of persistence based on cellular trajectory data. Here, we present a novel approach, the entropy of angular distribution , which combines cellular turning dynamics and Shannon entropy to explore the statistical and time-varying properties of persistence that strongly correlate with cellular migration modes. Our results reveal the changes in the persistence of multiple cell lines that are tightly regulated by both intra- and extracellular cues, including Arpin protein, collagen gel/substrate, and physical constraints. Significantly, some previously unreported distinctive details of persistence have also been captured, helping to elucidate how directional persistence is distributed and evolves in different cell populations. The analysis suggests that the entropy of angular distribution-based approach provides a powerful metric for evaluating directional persistence and enables us to better understand the relationships between cellular behaviors and multiscale cues, which also provides some insights into the migration dynamics of cell populations, such as collective cell invasion.

AttnPep: A Self-Attention-Based Deep Learning Method for Peptide Identification in Shotgun Proteomics.

In shotgun proteomics, the proteome search engine analyzes mass spectra obtained by experiments, and then a peptide-spectra match (PSM) is reported for each spectrum. However, most of the PSMs identified are incorrect, and therefore various postprocessing software have been developed for reranking the peptide identifications. Yet these methods suffer from issues such as dependency on distribution, reliance on shallow models, and limited effectiveness. In this work, we propose AttnPep, a deep learning model for rescoring PSM scores that utilizes the Self-Attention module. This module helps the neural network focus on features relevant to the classification of PSMs and ignore irrelevant features. This allows AttnPep to analyze the output of different search engines and improve PSM discrimination accuracy. We considered a PSM to be correct if it achieves a q-value <0.01 and compared AttnPep with existing mainstream software PeptideProphet, Percolator, and proteoTorch. The results indicated that AttnPep found an average increase in correct PSMs of 9.29% relative to the other methods. Additionally, AttnPep was able to better distinguish between correct and incorrect PSMs and found more synthetic peptides in the complex SWATH data set.

Predicting the potential human lncRNA-miRNA interactions based on graph convolution network with conditional random field.

Long non-coding RNA (lncRNA) and microRNA (miRNA) are two typical types of non-coding RNAs (ncRNAs), their interaction plays an important regulatory role in many biological processes. Exploring the interactions between unknown lncRNA and miRNA can help us better understand the functional expression between lncRNA and miRNA. At present, the interactions between lncRNA and miRNA are mainly obtained through biological experiments, but such experiments are often time-consuming and labor-intensive, it is necessary to design a computational method that can predict the interactions between lncRNA and miRNA. In this paper, we propose a method based on graph convolutional neural (GCN) network and conditional random field (CRF) for predicting human lncRNA-miRNA interactions, named GCNCRF. First, we construct a heterogeneous network using the known interactions of lncRNA and miRNA in the LncRNASNP2 database, the lncRNA/miRNA integration similarity network, and the lncRNA/miRNA feature matrix. Second, the initial embedding of nodes is obtained using a GCN network. A CRF set in the GCN hidden layer can update the obtained preliminary embeddings so that similar nodes have similar embeddings. At the same time, an attention mechanism is added to the CRF layer to reassign weights to nodes to better grasp the feature information of important nodes and ignore some nodes with less influence. Finally, the final embedding is decoded and scored through the decoding layer. Through a 5-fold cross-validation experiment, GCNCRF has an area under the receiver operating characteristic curve value of 0.947 on the main dataset, which has higher prediction accuracy than the other six state-of-the-art methods.

Microscopic origins of the crystallographically preferred growth in evaporation-induced colloidal crystals.

Unlike crystalline atomic and ionic solids, texture development due to crystallographically preferred growth in colloidal crystals is less studied. Here we investigate the underlying mechanisms of the texture evolution in an evaporation-induced colloidal assembly process through experiments, modeling, and theoretical analysis. In this widely used approach to obtain large-area colloidal crystals, the colloidal particles are driven to the meniscus via the evaporation of a solvent or matrix precursor solution where they close-pack to form a face-centered cubic colloidal assembly. Via two-dimensional large-area crystallographic mapping, we show that the initial crystal orientation is dominated by the interaction of particles with the meniscus, resulting in the expected coalignment of the close-packed direction with the local meniscus geometry. By combining with crystal structure analysis at a single-particle level, we further reveal that, at the later stage of self-assembly, however, the colloidal crystal undergoes a gradual rotation facilitated by geometrically necessary dislocations (GNDs) and achieves a large-area uniform crystallographic orientation with the close-packed direction perpendicular to the meniscus and parallel to the growth direction. Classical slip analysis, finite element-based mechanical simulation, computational colloidal assembly modeling, and continuum theory unequivocally show that these GNDs result from the tensile stress field along the meniscus direction due to the constrained shrinkage of the colloidal crystal during drying. The generation of GNDs with specific slip systems within individual grains leads to crystallographic rotation to accommodate the mechanical stress. The mechanistic understanding reported here can be utilized to control crystallographic features of colloidal assemblies, and may provide further insights into crystallographically preferred growth in synthetic, biological, and geological crystals.

Mitochondrial outer membrane permeabilization and inner membrane permeabilization in regulating apoptosis and inflammation.

Recent studies delineate an intimate crosstalk between apoptosis and inflammation. However, the dynamic mechanism linking them by mitochondrial membrane permeabilization remains elusive. Here, we construct a mathematical model consisting of four functional modules. Bifurcation analysis reveals that bistability stems from Bcl-2 family member interaction and time series shows that the time difference between Cyt c and mtDNA release is around 30 min, which are consistent with previous works. The model predicts that Bax aggregation kinetic determines cells to undergo apoptosis or inflammation, and that modulating the inhibitory effect of caspase 3 on IFN-ß production allows the concurrent occurrence of apoptosis and inflammation. This work provides a theoretical framework for exploring the mechanism of mitochondrial membrane permeabilization in controlling cell fate.

A physical understanding and quantification for the regulation of orexin on sleep.

The orexinergic neurons located in the lateral hypothalamus play a vital role in maintaining wakefulness and regulating sleep stability. Previous research has demonstrated that the absence of orexin (Orx) can trigger narcolepsy, a condition characterized by frequent shifts between wakefulness and sleep. However, the specific mechanisms and temporal patterns through which Orx regulates wakefulness/sleep are not fully understood. In this study, we developed a new model that combines the classical Phillips-Robinson sleep model with the Orx network. Our model incorporates a recently discovered indirect inhibition of Orx on sleep-promoting neurons in the ventrolateral preoptic nucleus. By integrating appropriate physiological parameters, our model successfully replicated the dynamic behavior of normal sleep under the influence of circadian drive and homeostatic processes. Furthermore, our results from the new sleep model unveiled two distinct effects of Orx: excitation of wake-active neurons and inhibition of sleep-active neurons. The excitation effect helps to sustain wakefulness, while the inhibition effect contributes to arousal, consistent with experimental findings [De Luca et al., Nat. Commun. 13, 4163 (2022)]. Moreover, we utilized the theory of potential landscapes to investigate the physical mechanisms underlying the frequent transitions observed in narcolepsy. The topography of the underlying landscape delineated the brain's capacity to transition between different states. Additionally, we examined the impact of Orx on barrier height. Our analysis demonstrated that a reduced level of Orx led to a bistable state with an extremely low threshold, contributing to the development of narcoleptic sleep disorder.

Biphasic regulation of transcriptional surge generated by the gene feedback loop in a two-component system.

MOTIVATION: Transcriptional surges generated by two-component systems (TCSs) have been observed experimentally in various bacteria. Suppression of the transcriptional surge may reduce the activity, virulence and drug resistance of bacteria. In order to investigate the general mechanisms, we use a PhoP/PhoQ TCS as a model system to derive a comprehensive mathematical modeling that governs the surge. PhoP is a response regulator, which serves as a transcription factor under a phosphorylation-dependent modulation by PhoQ, a histidine kinase. RESULTS: Our model reveals two major signaling pathways to modulate the phosphorylated PhoP (P-PhoP) level, one of which promotes the generation of P-PhoP, while the other depresses the level of P-PhoP. The competition between the P-PhoP-promoting and the P-PhoP-depressing pathways determines the generation of the P-PhoP surge. Furthermore, besides PhoQ, PhoP is also a bifunctional modulator that contributes to the dynamic control of P-PhoP state, leading to a biphasic regulation of the surge by the gene feedback loop. In summary, the mechanisms derived from the PhoP/PhoQ system for the transcriptional surges provide a better understanding on such a sophisticated signal transduction system and aid to develop new antimicrobial strategies targeting TCSs. AVAILABILITY AND IMPLEMENTATION: https://github.com/jianweishuai/TCS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

CITEMOXMBD: A flexible single-cell multimodal omics analysis framework to reveal the heterogeneity of immune cells.

Simultaneous measurement of multiple modalities in single-cell analysis, represented by CITE-seq, is a promising approach to link transcriptional changes to cellular phenotype and function, requiring new computational methods to define cellular subtypes and states based on multiple data types. Here, we design a flexible single-cell multimodal analysis framework, called CITEMO, to integrate the transcriptome and antibody-derived tags (ADT) data to capture cell heterogeneity from the multi omics perspective. CITEMO uses Principal Component Analysis (PCA) to obtain a low-dimensional representation of the transcriptome and ADT, respectively, and then employs PCA again to integrate these low-dimensional multimodal data for downstream analysis. To investigate the effectiveness of the CITEMO framework, we apply CITEMO to analyse the cell subtypes of Cord Blood Mononuclear Cells (CBMC) samples. Results show that the CITEMO framework can comprehensively analyse single-cell multimodal samples and accurately identify cell subtypes. Besides, we find some specific immune cells that co-express multiple ADT markers. To better describe the co-expression phenomenon, we introduce the co-expression entropy to measure the heterogeneous distribution of the ADT combinations. To further validate the robustness of the CITEMO framework, we analyse Human Bone Marrow Cell (HBMC) samples and identify different states of the same cell type. CITEMO has an excellent performance in identifying cell subtypes and states for multimodal omics data. We suggest that the flexible design idea of CITEMO can be an inspiration for other single-cell multimodal tasks. The complete source code and dataset of the CITEMO framework can be obtained from https://github.com/studentiz/CITEMO.

Network modeling-based identification of the switching targets between pyroptosis and secondary pyroptosis.

The newly identified cell death type, pyroptosis plays crucial roles in various diseases. Most recently, mounting evidence accumulates that pyroptotic signaling is highly correlated with coronavirus disease 2019 (COVID-19). Thus, understanding the induction of the pyroptotic signaling and dissecting the detail molecular control mechanisms are urgently needed. Based on recent experimental studies, a core regulatory model of the pyroptotic signaling is constructed to investigate the intricate crosstalk dynamics between the two cell death types, i.e., pyroptosis and secondary pyroptosis. The model well reproduces the experimental observations under different conditions. Sensitivity analysis determines that only the expression level of caspase-1 or GSDMD has the potential to individually change death modes. The decrease of caspase-1 or GSDMD level switches cell death from pyroptosis to secondary pyroptosis. Besides, eight biochemical reactions are identified that can efficiently switch death modes. While from the viewpoint of bifurcation analysis, the expression level of caspase-3 is further identified and twelve biochemical reactions are obtained. The coexistence of pyroptosis and secondary pyroptosis is predicted to be observed not only within the bistable range, but also within proper monostable range, presenting two potential different control mechanisms. Combined with the landscape theory, we further explore the stochastic dynamic and global stability of the pyroptotic system, accurately quantifying how each component mediates the individual occurrence probability of pyroptosis and secondary pyroptosis. Overall, this study sheds new light on the intricate crosstalk of the pyroptotic signaling and uncovers the regulatory mechanisms of various stable state transitions, providing potential clues to guide the development for prevention and treatment of pyroptosis-related diseases.

Shannon entropy for time-varying persistence of cell migration.

Cell migration, which can be significantly affected by intracellular signaling pathways and extracellular matrix, plays a crucial role in many physiological and pathological processes. Cell migration is typically modeled as a persistent random walk, which depends on two critical motility parameters, i.e., migration speed and persistence time. It is generally very challenging to efficiently and accurately quantify the migration dynamics from noisy experimental data. Here, we introduce the normalized Shannon entropy (SE) based on the FPS of cellular velocity autocovariance function to quantify migration dynamics. The SE introduced here possesses a similar physical interpretation as the Gibbs entropy for thermal systems in that SE naturally reflects the degree of order or randomness of cellular migration, attaining the maximal value of unity for purely diffusive migration (i.e., SE = 1 for the most "random" dynamics) and the minimal value of 0 for purely ballistic dynamics (i.e., SE = 0 for the most "ordered" dynamics). We also find that SE is strongly correlated with the migration persistence but is less sensitive to the migration speed. Moreover, we introduce the time-varying SE based on the WPS of cellular dynamics and demonstrate its superior utility to characterize the time-dependent persistence of cell migration, which typically results from complex and time-varying intra- or extracellular mechanisms. We employ our approach to analyze experimental data of in vitro cell migration regulated by distinct intracellular and extracellular mechanisms, exhibiting a rich spectrum of dynamic characteristics. Our analysis indicates that the SE and wavelet transform (i.e., SE-based approach) offers a simple and efficient tool to quantify cell migration dynamics in complex microenvironment.

Different response modes and cooperation modulations of blue-light receptors in photomorphogenesis.

Cryptochromes photoreceptors, CRY1 and CRY2 in Arabidopsis, mediate blue light responses in plants and metazoa. The signalling interactions underlying photomorphogenesis of cryptochromes action have been extensively studied in experiment, expecting a systematical analysis of the dynamic mechanisms of photosensory signalling network from a global view. In this study, we developed a signalling network model to quantitatively investigate the different response modes and cooperation modulations on photomorphogenesis for CRY1 and CRY2 under blue light. The model shows that the different modes of time-dependent and fluence-rate-dependent phosphorylations for CRY1 and CRY2 are originated from their different phosphorylation rates and degradation rates. Our study indicates that, due to the strong association between blue-light inhibitor of cryptochromes (BIC) and CRY2, BIC negatively modulates CRY2 phosphorylation, which was confirmed by our experiment. The experiment also validated the model prediction that the time-dependent BIC-CRY1 and the fluence-rate-dependent BIC-CRY2 are both bell-shaped under blue light. Importantly, the model proposes that the COP1-SPA abundance can strongly inhibit the phosphorylation response of CRY2, resulting in the positive regulation of CRY2 phosphorylation by CRY1 through COP1-SPA. The model also predicts that the CRY1-HY5 axis, rather than CRY2-HY5 pathway, plays a dominant role in blue-light-dependent photomorphogenesis.

Deriving time-varying cellular motility parameters via wavelet analysis.

Cell migration, which is regulated by intracellular signaling pathways (ICSP) and extracellular matrix (ECM), plays an indispensable role in many physiological and pathological process such as normal tissue development and cancer metastasis. However, there is a lack of rigorous and quantitative tools for analyzing the time-varying characteristics of cell migration in heterogeneous microenvironment, resulted from, e.g. the time-dependent local stiffness due to microstructural remodeling by migrating cells. Here, we develop a wavelet-analysis approach to derive the time-dependent motility parameters from cell migration trajectories, based on the time-varying persistent random walk model. In particular, the wavelet denoising and wavelet transform are employed to analyze migration velocities and obtain the wavelet power spectrum. Subsequently, the time-dependent motility parameters are derived via Lorentzian power spectrum. Our results based on synthetic data indicate the superiority of the method for estimating the intrinsic transient motility parameters, robust against a variety of stochastic noises. We also carry out a systematic parameter study and elaborate the effects of parameter selection on the performance of the method. Moreover, we demonstrate the utility of our approach via analyzing experimental data ofin vitrocell migration in distinct microenvironments, including the migration of MDA-MB-231 cells in confined micro-channel arrays and correlated migration of MCF-10A cells due to ECM-mediated mechanical coupling. Our analysis shows that our approach can be as a powerful tool to accurately derive the time-dependent motility parameters, and further analyze the time-dependent characteristics of cell migration regulated by complex microenvironment.

Formation of oxygen vacancies in Li2FeSiO4: first-principles calculations.

The formation of oxygen vacancies could affect various properties of oxides. Herein we have investigated the formation energies of an oxygen vacancy (VO) with the relevant charge states in bulk Pnma-Li2FeSiO4 using first-principles calculations. The formation energies of the VO are essentially dependent on the atomic chemical potentials that represent the experimental conditions. The calculated formation energies of an oxygen vacancy in different charge states indicate that it would be energetically favorable to fully ionize the oxygen vacancy in Li2FeSiO4. The presence of VO is accompanied by a distinct redistribution of the electronic charge densities only around the Fe and Si ions next to the O-vacancy site, which shows a very local influence on the host material arising from VO. This local characteristic is also confirmed by the calculated partial densities of states (PDOS). We also studied the influence of substitutional (MnFe and CoFe) and cation vacancy defects (i.e., VFe and VLi) in the vicinity of an O-vacancy on the formation of an O-vacancy, respectively. We find that the calculated interaction energies between these defects and the oxygen vacancy are all negative, which implies that the formation of an oxygen vacancy becomes easier when the above defects are introduced. Compared to the substitutional defects, the interaction energies between the vacancy defects and the oxygen vacancy are significantly larger. Among them, the interaction energy between VFe and VO is the largest.

Cell death modes are specified by the crosstalk dynamics within pyroptotic and apoptotic signaling.

The crosstalk between pyroptosis and apoptosis pathways plays crucial roles in homeostasis, cancer, and other pathologies. However, its molecular regulatory mechanisms for cell death decision-making remain to be elucidated. Based on the recent experimental studies, we developed a core regulatory network model of the crosstalk between pyroptosis and apoptosis pathways. Sensitivity analysis and bifurcation analysis were performed to assess the death mode switching of the network. Both the approaches determined that only the level of caspase-1 or gasdermin D (GSDMD) has the potential to individually change death modes. The decrease of caspase-1 or GSDMD switches cell death from pyroptosis to apoptosis. Seven biochemical reactions among the 21 reactions in total that are essential for determining cell death modes are identified by using sensitivity analysis. While with bifurcation analysis of state transitions, nine reactions are suggested to be able to efficiently switch death modes. Monostability, bistability, and tristability are observed under different conditions. We found that only the reaction that caspase-1 activation induced by stimuli can trigger tristability. Six and two of the nine reactions are identified to be able to induce bistability and monostability, respectively. Moreover, the concurrence of pyroptosis and apoptosis is observed not only within proper bistable ranges, but also within tristable ranges, implying two potentially distinct regulatory mechanisms. Taken together, this work sheds new light on the crosstalk between pyroptosis and apoptosis and uncovers the regulatory mechanisms of various stable state transitions, which play important roles for the development of potential control strategies for disease prevention and treatment.

Systematic Assessment of the Effect of Internal Library in Targeted Analysis of SWATH-MS.

Targeted analysis of sequential window acquisition of all theoretical mass spectra (SWATH-MS) requires the spectral library, which can be generated by shotgun mass spectrometry (MS) or by the pseudo-spectra files directly obtained from SWATH-MS data. The external library generated by shotgun MS is employed in most SWATH-MS research. However, performance of the internal library, which is constructed by pseudo-spectra files, in the targeted analysis of SWATH-MS has not been systemically evaluated. Here, we show that up to 40% of the peptides detected by the internal library were not overlapped with those detected by the external library for most SWATH-MS data sets. However, the internal library did not identify extra phosphopeptides compared with the external library for phosphoproteomic SWATH-MS data. Therefore, the internal library should be incorporated into the external library for targeted analysis of nonphosphoproteomic SWATH-MS, given that it can significantly increase the number of peptides of SWATH-MS without requiring additional instrument measurement time.