Resolving Cell Fate Decisions during Somatic Cell Reprogramming by Single-Cell RNA-Seq.

Somatic cells can be reprogrammed into induced pluripotent stem cells (iPSCs), which is a highly heterogeneous process. Here we report the cell fate continuum during somatic cell reprogramming at single-cell resolution. We first develop SOT to analyze cell fate continuum from Oct4/Sox2/Klf4- or OSK-mediated reprogramming and show that cells bifurcate into two categories, reprogramming potential (RP) or non-reprogramming (NR). We further show that Klf4 contributes to Cd34+/Fxyd5+/Psca+ keratinocyte-like NR fate and that IFN-γ impedes the final transition to chimera-competent pluripotency along the RP cells. We analyze more than 150,000 single cells from both OSK and chemical reprograming and identify additional NR/RP bifurcation points. Our work reveals a generic bifurcation model for cell fate decisions during somatic cell reprogramming that may be applicable to other systems and inspire further improvements for reprogramming.

Regeneration of the human segmentation clock in somitoids in vitro.

Each vertebrate species appears to have a unique timing mechanism for forming somites along the vertebral column, and the process in human remains poorly understood at the molecular level due to technical and ethical limitations. Here, we report the reconstitution of human segmentation clock by direct reprogramming. We first reprogrammed human urine epithelial cells to a presomitic mesoderm (PSM) state capable of long-term self-renewal and formation of somitoids with an anterior-to-posterior axis. By inserting the RNA reporter Pepper into HES7 and MESP2 loci of these iPSM cells, we show that both transcripts oscillate in the resulting somitoids at ~5 h/cycle. GFP-tagged endogenous HES7 protein moves along the anterior-to-posterior axis during somitoid formation. The geo-sequencing analysis further confirmed anterior-to-posterior polarity and revealed the localized expression of WNT, BMP, FGF, and RA signaling molecules and HOXA-D family members. Our study demonstrates the direct reconstitution of human segmentation clock from somatic cells, which may allow future dissection of the mechanism and components of such a clock and aid regenerative medicine.

A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation.

Artificial intelligence (AI)-driven methods can vastly improve the historically costly drug design process, with various generative models already in widespread use. Generative models for de novo drug design, in particular, focus on the creation of novel biological compounds entirely from scratch, representing a promising future direction. Rapid development in the field, combined with the inherent complexity of the drug design process, creates a difficult landscape for new researchers to enter. In this survey, we organize de novo drug design into two overarching themes: small molecule and protein generation. Within each theme, we identify a variety of subtasks and applications, highlighting important datasets, benchmarks, and model architectures and comparing the performance of top models. We take a broad approach to AI-driven drug design, allowing for both micro-level comparisons of various methods within each subtask and macro-level observations across different fields. We discuss parallel challenges and approaches between the two applications and highlight future directions for AI-driven de novo drug design as a whole. An organized repository of all covered sources is available at https://github.com/gersteinlab/GenAI4Drug.

pathMap: a path-based mapping tool for long noisy reads with high sensitivity.

With the rapid development of single-molecule sequencing (SMS) technologies, the output read length is continuously increasing. Mapping such reads onto a reference genome is one of the most fundamental tasks in sequence analysis. Mapping sensitivity is becoming a major concern since high sensitivity can detect more aligned regions on the reference and obtain more aligned bases, which are useful for downstream analysis. In this study, we present pathMap, a novel k-mer graph-based mapper that is specifically designed for mapping SMS reads with high sensitivity. By viewing the alignment chain as a path containing as many anchors as possible in the matched k-mer graph, pathMap treats chaining as a path selection problem in the directed graph. pathMap iteratively searches the longest path in the remaining nodes; more candidate chains with high quality can be effectively detected and aligned. Compared to other state-of-the-art mapping methods such as minimap2 and Winnowmap2, experiment results on simulated and real-life datasets demonstrate that pathMap obtains the number of mapped chains at least 11.50% more than its closest competitor and increases the mapping sensitivity by 17.28% and 13.84% of bases over the next-best mapper for Pacific Biosciences and Oxford Nanopore sequencing data, respectively. In addition, pathMap is more robust to sequence errors and more sensitive to species- and strain-specific identification of pathogens using MinION reads.

Proteomic Dynamics of Breast Cancer Cell Lines Identifies Potential Therapeutic Protein Targets.

Treatment and relevant targets for breast cancer (BC) remain limited, especially for triple-negative BC (TNBC). We identified 6091 proteins of 76 human BC cell lines using data-independent acquisition (DIA). Integrating our proteomic findings with prior multi-omics datasets, we found that including proteomics data improved drug sensitivity predictions and provided insights into the mechanisms of action. We subsequently profiled the proteomic changes in nine cell lines (five TNBC and four non-TNBC) treated with EGFR/AKT/mTOR inhibitors. In TNBC, metabolism pathways were dysregulated after EGFR/mTOR inhibitor treatment, while RNA modification and cell cycle pathways were affected by AKT inhibitor. This systematic multi-omics and in-depth analysis of the proteome of BC cells can help prioritize potential therapeutic targets and provide insights into adaptive resistance in TNBC.

The oxidized phospholipid PGPC impairs endothelial function by promoting endothelial cell ferroptosis via FABP3.

Ferroptosis is a novel cell death mechanism that is mediated by iron-dependent lipid peroxidation. It may be involved in atherosclerosis development. Products of phospholipid oxidation play a key role in atherosclerosis. 1-palmitoyl-2-glutaroyl-sn-glycero-3-phosphocholine (PGPC) is a phospholipid oxidation product present in atherosclerotic lesions. It remains unclear whether PGPC causes atherosclerosis by inducing endothelial cell ferroptosis. In this study, human umbilical vein endothelial cells (HUVECs) were treated with PGPC. Intracellular levels of ferrous iron, lipid peroxidation, superoxide anions (O2•-), and glutathione were detected, and expression of fatty acid binding protein-3 (FABP3), glutathione peroxidase 4 (GPX4), and CD36 were measured. Additionally, the mitochondrial membrane potential (MMP) was determined. Aortas from C57BL6 mice were isolated for vasodilation testing. Results showed that PGPC increased ferrous iron levels, the production of lipid peroxidation and O2•-, and FABP3 expression. However, PGPC inhibited the expression of GPX4 and glutathione production and destroyed normal MMP. These effects were also blocked by ferrostatin-1, an inhibitor of ferroptosis. FABP3 silencing significantly reversed the effect of PGPC. Furthermore, PGPC stimulated CD36 expression. Conversely, CD36 silencing reversed the effects of PGPC, including PGPC-induced FABP3 expression. Importantly, E06, a direct inhibitor of the oxidized 1-palmitoyl-2-arachidonoyl-phosphatidylcholine IgM natural antibody, inhibited the effects of PGPC. Finally, PGPC impaired endothelium-dependent vasodilation, ferrostatin-1 or FABP3 inhibitors inhibited this impairment. Our data demonstrate that PGPC impairs endothelial function by inducing endothelial cell ferroptosis through the CD36 receptor to increase FABP3 expression. Our findings provide new insights into the mechanisms of atherosclerosis and a therapeutic target for atherosclerosis.

Oncogenic and immunological roles of RACGAP1 in pan-cancer and its potential value in nasopharyngeal carcinoma.

A particular GTPase-activating protein called RACGAP1 is involved in apoptosis, proliferation, invasion, metastasis, and drug resistance in a variety of malignancies. Nevertheless, the role of RACGAP1 in pan-cancer was less studied, and its value of the expression and prognostic of nasopharyngeal carcinoma (NPC) has not been explored. Hence, the goal of this study was to investigate the oncogenic and immunological roles of RACGAP1 in various cancers and its potential value in NPC. We comprehensively analyzed RACGAP1 expression, prognostic value, function, methylation levels, relationship with immune cells, immune infiltration, and immunotherapy response in pan-cancer utilizing multiple databases. The results discovered that RACGAP1 expression was elevated in most cancers and suggested poor prognosis, which could be related to the involvement of RACGAP1 in various cancer-related pathways such as the cell cycle and correlated with RACGAP1 methylation levels, immune cell infiltration and reaction to immunotherapy, and chemoresistance. RACGAP1 could inhibit anti-tumor immunity and immunotherapy responses by fostering immune cell infiltration and cytotoxic T lymphocyte dysfunction. Significantly, we validated that RACGAP1 mRNA and protein were highly expressed in NPC. The Gene Expression Omnibus database revealed that elevated RACGAP1 expression was associated with shorter PFS in patients with NPC, and RACGAP1 potentially influenced cell cycle progression, DNA replication, metabolism, and immune-related pathways, resulting in the recurrence and metastasis of NPC. This study indicated that RACGAP1 could be a potential biomarker in pan-cancer and NPC.

Reducing Quantitative Uncertainty Caused by Data Processing in Untargeted Metabolomics.

Processing liquid chromatography-mass spectrometry-based metabolomics data using computational programs often introduces additional quantitative uncertainty, termed computational variation in a previous work. This work develops a computational solution to automatically recognize metabolic features with computational variation in a metabolomics data set. This tool, AVIR (short for "Accurate eValuation of alIgnment and integRation"), is a support vector machine-based machine learning strategy (https://github.com/HuanLab/AVIR). The rationale is that metabolic features with computational variation have a poor correlation between chromatographic peak area and peak height-based quantifications across the samples in a study. AVIR was trained on a set of 696 manually curated metabolic features and achieved an accuracy of 94% in a 10-fold cross-validation. When tested on various external data sets from public metabolomics repositories, AVIR demonstrated an accuracy range of 84%-97%. Finally, tested on a large-scale metabolomics study, AVIR clearly indicated features with computational variation and thus guided us to manually correct them. Our results show that 75.3% of the samples with computational variation had a relative intensity difference of over 20% after correction. This demonstrates the critical role of AVIR in reducing computational variation to improve quantitative certainty in untargeted metabolomics analysis.

Association of vaccine-specific regulatory T cells with reduced antibody response to repeated influenza vaccination.

Repeated annual influenza vaccinations have been associated with reduced vaccine-induced antibody responses. This prospective study aimed to explore the role of vaccine antigen-specific regulatory T (Treg) cells in antibody response to repeated annual influenza vaccination. We analyzed pre- and postvaccination hemagglutination inhibition (HI) titers, seroconversion rates, seroprotection rates, vaccine antigen hemagglutinin (HA)-specific Treg cells, and conventional T (Tconv) cells. We compared these parameters between vaccinees with or without vaccine-induced seroconversion. Our multivariate logistic regression revealed that prior vaccination was significantly associated with a decreased likelihood of achieving seroconversion for both H1N1(adjusted OR, 0.03; 95% CI, 0.01-0.13) and H3N2 (adjusted OR, 0.09; 95% CI, 0.03-0.30). Furthermore, individuals who received repeated vaccinations had significantly higher levels of pre-existing HA-specific Treg cells than those who did not. We also found that vaccine-induced fold-increases in HI titers and seroconversion were negatively correlated with pre-existing HA-specific Treg cells and positively correlated with the ratio of Tconv to Treg cells. Overall, our findings suggest that repeated annual influenza vaccination is associated with a lower vaccine-induced antibody response and a higher frequency of vaccine-specific Treg cells. However, a lower frequency of pre-existing Treg cells correlates with a higher postvaccination antibody response.

Single-nucleus transcriptome analysis identifies a novel FKBP5+ endothelial cell subtype involved in endothelial-to-mesenchymal transition in adipose tissue during aging.

Age-associated adipose tissue (AT) dysfunction is multifactorial and often leads to detrimental health consequences. AT is highly vascularized and endothelial cells (ECs) has been recently identified as a key regulator in the homeostasis of AT. However, the alteration of cell composition in AT during aging and the communication between endothelial cells and adipocytes remain poorly understood. In this study, we take advantage of single nucleus RNA sequencing analysis, and discovered a group of FKBP5+ ECs specifically resident in aged AT. Of interest, FKBP5+ ECs exhibited the potential for endothelial-to-mesenchymal transition (EndoMT) and exhibited a critical role in regulating adipocytes. Furthermore, lineage tracing experiments demonstrated that ECs in aged AT tend to express FKBP5 and undergo EndoMT with progressive loss of endothelial marker. This study may provide a basis for a new mechanism of microvascular ECs-induced AT dysfunction during aging.

Effective Bidirectional Mott-Schottky Catalysts Derived from Spent LiFePO4 Cathodes for Robust Lithium-Sulfur Batteries.

It is deemed as a tough yet profound project to comprehensively cope with a range of detrimental problems of lithium-sulfur batteries (LSBs), mainly pertaining to the shuttle effect of lithium polysulfides (LiPSs) and sluggish sulfur conversion. Herein, a Co2P-Fe2P@N-doped carbon (Co2P-Fe2P@NC) Mott-Schottky catalyst is introduced to enable bidirectionally stimulated sulfur conversion. This catalyst is prepared by simple carbothermal reduction of spent LiFePO4 cathode and LiCoO2. The experimental and theoretical calculation results indicate that thanks to unique surface/interface properties derived from the Mott-Schottky effect, full anchoring of LiPSs, mediated Li2S nucleation/dissolution, and bidirectionally expedited "solid⇌liquid⇌solid" kinetics can be harvested. Consequently, the S/Co2P-Fe2P@NC manifests high reversible capacity (1569.9 mAh g-1), superb rate response (808.9 mAh g-1 at 3C), and stable cycling (a low decay rate of 0.06% within 600 cycles at 3C). Moreover, desirable capacity (5.35 mAh cm-2) and cycle stability are still available under high sulfur loadings (4-5 mg cm-2) and lean electrolyte (8 µL mg-1) conditions. Furthermore, the as-proposed universal synthetic route can be extended to the preparation of other catalysts such as Mn2P-Fe2P@NC from spent LiFePO4 and MnO2. This work unlocks the potential of carbothermal reduction phosphating to synthesize bidirectional catalysts for robust LSBs.

MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations.

MOTIVATION: MicroRNAs (miRNAs), as critical regulators, are involved in various fundamental and vital biological processes, and their abnormalities are closely related to human diseases. Predicting disease-related miRNAs is beneficial to uncovering new biomarkers for the prevention, detection, prognosis, diagnosis and treatment of complex diseases. RESULTS: In this study, we propose a multi-view Laplacian regularized deep factorization machine (DeepFM) model, MLRDFM, to predict novel miRNA-disease associations while improving the standard DeepFM. Specifically, MLRDFM improves DeepFM from two aspects: first, MLRDFM takes the relationships among items into consideration by regularizing their embedding features via their similarity-based Laplacians. In this study, miRNA Laplacian regularization integrates four types of miRNA similarity, while disease Laplacian regularization integrates two types of disease similarity. Second, to judiciously train our model, Laplacian eigenmaps are utilized to initialize the weights in the dense embedding layer. The experimental results on the latest HMDD v3.2 dataset show that MLRDFM improves the performance and reduces the overfitting phenomenon of DeepFM. Besides, MLRDFM is greatly superior to the state-of-the-art models in miRNA-disease association prediction in terms of different evaluation metrics with the 5-fold cross-validation. Furthermore, case studies further demonstrate the effectiveness of MLRDFM.

Predicting drug-drug interactions by graph convolutional network with multi-kernel.

Drug repositioning is proposed to find novel usages for existing drugs. Among many types of drug repositioning approaches, predicting drug-drug interactions (DDIs) helps explore the pharmacological functions of drugs and achieves potential drugs for novel treatments. A number of models have been applied to predict DDIs. The DDI network, which is constructed from the known DDIs, is a common part in many of the existing methods. However, the functions of DDIs are different, and thus integrating them in a single DDI graph may overlook some useful information. We propose a graph convolutional network with multi-kernel (GCNMK) to predict potential DDIs. GCNMK adopts two DDI graph kernels for the graph convolutional layers, namely, increased DDI graph consisting of 'increase'-related DDIs and decreased DDI graph consisting of 'decrease'-related DDIs. The learned drug features are fed into a block with three fully connected layers for the DDI prediction. We compare various types of drug features, whereas the target feature of drugs outperforms all other types of features and their concatenated features. In comparison with three different DDI prediction methods, our proposed GCNMK achieves the best performance in terms of area under receiver operating characteristic curve and area under precision-recall curve. In case studies, we identify the top 20 potential DDIs from all unknown DDIs, and the top 10 potential DDIs from the unknown DDIs among breast, colorectal and lung neoplasms-related drugs. Most of them have evidence to support the existence of their interactions. fangxiang.wu@usask.ca.

HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure.

MOTIVATION: Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. RESULTS: We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. CONCLUSIONS: The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

Biomedical data, computational methods and tools for evaluating disease-disease associations.

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

DeepLncLoc: a deep learning framework for long non-coding RNA subcellular localization prediction based on subsequence embedding.

Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. A growing amount of evidence reveals that subcellular localization of lncRNAs can provide valuable insights into their biological functions. Existing computational methods for predicting lncRNA subcellular localization use k-mer features to encode lncRNA sequences. However, the sequence order information is lost by using only k-mer features. We proposed a deep learning framework, DeepLncLoc, to predict lncRNA subcellular localization. In DeepLncLoc, we introduced a new subsequence embedding method that keeps the order information of lncRNA sequences. The subsequence embedding method first divides a sequence into some consecutive subsequences and then extracts the patterns of each subsequence, last combines these patterns to obtain a complete representation of the lncRNA sequence. After that, a text convolutional neural network is employed to learn high-level features and perform the prediction task. Compared with traditional machine learning models, popular representation methods and existing predictors, DeepLncLoc achieved better performance, which shows that DeepLncLoc could effectively predict lncRNA subcellular localization. Our study not only presented a novel computational model for predicting lncRNA subcellular localization but also introduced a new subsequence embedding method which is expected to be applied in other sequence-based prediction tasks. The DeepLncLoc web server is freely accessible at http://bioinformatics.csu.edu.cn/DeepLncLoc/, and source code and datasets can be downloaded from https://github.com/CSUBioGroup/DeepLncLoc.

An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease.

Alzheimer's disease (AD) has a strong genetic predisposition. However, its risk genes remain incompletely identified. We developed an Alzheimer's brain gene network-based approach to predict AD-associated genes by leveraging the functional pattern of known AD-associated genes. Our constructed network outperformed existing networks in predicting AD genes. We then systematically validated the predictions using independent genetic, transcriptomic, proteomic data, neuropathological and clinical data. First, top-ranked genes were enriched in AD-associated pathways. Second, using external gene expression data from the Mount Sinai Brain Bank study, we found that the top-ranked genes were significantly associated with neuropathological and clinical traits, including the Consortium to Establish a Registry for Alzheimer's Disease score, Braak stage score and clinical dementia rating. The analysis of Alzheimer's brain single-cell RNA-seq data revealed cell-type-specific association of predicted genes with early pathology of AD. Third, by interrogating proteomic data in the Religious Orders Study and Memory and Aging Project and Baltimore Longitudinal Study of Aging studies, we observed a significant association of protein expression level with cognitive function and AD clinical severity. The network, method and predictions could become a valuable resource to advance the identification of risk genes for AD.

DeepCellEss: cell line-specific essential protein prediction with attention-based interpretable deep learning.

MOTIVATION: Protein essentiality is usually accepted to be a conditional trait and strongly affected by cellular environments. However, existing computational methods often do not take such characteristics into account, preferring to incorporate all available data and train a general model for all cell lines. In addition, the lack of model interpretability limits further exploration and analysis of essential protein predictions. RESULTS: In this study, we proposed DeepCellEss, a sequence-based interpretable deep learning framework for cell line-specific essential protein predictions. DeepCellEss utilizes a convolutional neural network and bidirectional long short-term memory to learn short- and long-range latent information from protein sequences. Further, a multi-head self-attention mechanism is used to provide residue-level model interpretability. For model construction, we collected extremely large-scale benchmark datasets across 323 cell lines. Extensive computational experiments demonstrate that DeepCellEss yields effective prediction performance for different cell lines and outperforms existing sequence-based methods as well as network-based centrality measures. Finally, we conducted some case studies to illustrate the necessity of considering specific cell lines and the superiority of DeepCellEss. We believe that DeepCellEss can serve as a useful tool for predicting essential proteins across different cell lines. AVAILABILITY AND IMPLEMENTATION: The DeepCellEss web server is available at http://csuligroup.com:8000/DeepCellEss. The source code and data underlying this study can be obtained from https://github.com/CSUBioGroup/DeepCellEss. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

invMap: a sensitive mapping tool for long noisy reads with inversion structural variants.

MOTIVATION: Longer reads produced by PacBio or Oxford Nanopore sequencers could more frequently span the breakpoints of structural variations (SVs) than shorter reads. Therefore, existing long-read mapping methods often generate wrong alignments and variant calls. Compared to deletions and insertions, inversion events are more difficult to be detected since the anchors in inversion regions are nonlinear to those in SV-free regions. To address this issue, this study presents a novel long-read mapping algorithm (named as invMap). RESULTS: For each long noisy read, invMap first locates the aligned region with a specifically designed scoring method for chaining, then checks the remaining anchors in the aligned region to discover potential inversions. We benchmark invMap on simulated datasets across different genomes and sequencing coverages, experimental results demonstrate that invMap is more accurate to locate aligned regions and call SVs for inversions than the competing methods. The real human genome sequencing dataset of NA12878 illustrates that invMap can effectively find more candidate variant calls for inversions than the competing methods. AVAILABILITY AND IMPLEMENTATION: The invMap software is available at https://github.com/zhang134/invMap.git.

Dynamic phase-differencing profilometry with number-theoretical phase unwrapping and interleaved projection.

High-speed 3D measurement is receiving increasing attention. However, simultaneously achieving high computational efficiency, algorithmic robustness, and reconstructing ratio is challenging. Therefore, a dynamic phase-differencing profilometry (DPDP) is proposed. By capturing the minimum three phase-shifting sinusoidal deformed patterns and establishing a brand-new model, the phase difference between the object on the reference plane and the reference plane is directly resolved to effectively improve computational efficiency. Although it is wrapped, by using only two auxiliary complementary gratings with a purposely designed lower frequency, a DPDP-based number-theoretical temporal phase unwrapping (NT-TPU) algorithm is also proposed to unwrap the wrapped phase difference rather than the phase itself with high robustness. Furthermore, compared to existing PSP-based NT-TPU, the proposed NT-TPU can normally work under more relaxed restrictions. In order to accomplish a high reconstructing ratio, a pentabasic interleaved projection (PIP) strategy based on time division multiplexing is proposed. It can improve the reconstructing ratio from one reconstruction per every five patterns to an equivalent of one reconstruction per every 1.67 patterns. Experimental results demonstrate that the proposed method achieves high computational efficiency, high algorithmic robustness, and high reconstructing ratio simultaneously and has prospective application in high-speed 3D measurement.