Biology subjects

FENG, X.

Publications and source records attributed to FENG, X..

5 recordsLinked to original sources

bioRxiv2025

Biologically Informative NA Deconvolution (BIND) excavates hidden features of the proteome from missing values in large-scale datasets

The fast-advancing mass spectrometry and related technologies have greatly extended the depth of coverage in large-scale proteomics studies, including single-cell applications. As sample numbers grow rapidly, it is often challenging to interpret the proteins with missing values that are often presented as "NA" (not available). It could be the evidence of no expression, low expression below the detection threshold, or false negative detection due to technical issues. Existing methods for missing values imputation, while generally useful, rarely consider the non-random NA values that inform biological significance. In the current study, we developed Biologically Informative NA Deconvolution (BIND) that applies an adaptive neighborhood-based modeling to deconvolve the nature of NAs as "biological" (low/no expression) or technical (experimental errors). Applying to multiple cell line datasets and human tissue extracellular vesicle datasets, BIND excavated the NAs that indicated "hallmark absence" of unique proteins. This led to improvements in protein-protein interaction analysis and the identification of novel disease biomarkers. To facilitate its public accessibility, we compiled BIND into a web server that features functional online operations and interactive visualizations. Furthermore, we demonstrated that the BIND server could deconvolve the NAs and improve the analyses of single-cell proteomics datasets. Overall, BIND delineates the biological significance of missing values rather than treating them as a burden, providing a critical perspective for understanding the complex proteome in various biological contexts. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=144 SRC="FIGDIR/small/660508v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@1a9d7c4org.highwire.dtl.DTLVardef@194b2d9org.highwire.dtl.DTLVardef@169d3bborg.highwire.dtl.DTLVardef@cba0de_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2023

Reversibly-bonded Microfluidic Devices for Stable Cell Culture and Rapid, Gentle Cell Extraction

Microfluidics chips have emerged as significant tools in cell culture due to their capacity for supporting cells to adopt more physiologically relevant morphology in 3D compared with traditional cell culture in 2D. Currently, irreversible bonding methods commonly used in chip fabrication mean that chips cannot be detached from their substrate without destroying the chip structure, which makes it challenging to do further analysis on cells that have been cultured on-chip. Some reversible bonding techniques exist but are restricted to certain materials, or require complex processing procedures. Here, we demonstrate a simple and reversible polydimethylsiloxane (PDMS)-polystyrene (PS) bonding technique that allows devices to withstand extended operation while pressurized, and supports long-term stable cell cultures. Importantly, it allows rapid and gentle live cell extraction for further downstream manipulation and characterization after long-term on-chip culturing, or even further subculturing. Our new approach could greatly facilitate microfluidic chip-based tissue and cell cultures, overcoming current analytical limitations and opening up new avenues for downstream uses of on-chip cultures, including 3D-engineered tissue structures for biomedical applications.

bioengineering↗

bioRxiv2023

Brain mechanisms of reversible symbolic reference:a potential singularity of the human brain

The emergence of symbolic thinking has been proposed as a dominant cognitive criterion to distinguish humans from other primates during hominization. Although the proper definition of a symbol has been the subject of much debate, one of its simplest features is bidirectional attachment: the content is accessible from the symbol, and vice versa. Behavioural observations scattered over the past four decades suggest that this criterion might not be met in non-human primates, as they fail to generalise an association learned in one temporal order (A to B) to the reverse order (B to A). Here, we designed an implicit fMRI test to investigate the neural mechanisms of arbitrary audio-visual and visual-visual pairing in monkeys and humans and probe their spontaneous reversibility. After learning a unidirectional association, humans showed surprise signals when this learned association was violated. Crucially, this effect occurred spontaneously in both learned and reversed directions, within an extended network of high-level brain areas, including, but also going beyond, the language network. In monkeys, by contrast, violations of association effects occurred solely in the learned direction and were largely confined to sensory areas. We propose that a human-specific brain network may have evolved the capacity for reversible symbolic reference.

neuroscience↗

bioRxiv2022

Resolving single-cell copy number profiling for large datasets

The advances of single-cell DNA sequencing (scDNA-seq) enable us to characterize the genetic heterogeneity of cancer cells. However, the high noise and low coverage of scDNA-seq impede the estimation of copy number variations (CNVs). In addition, existing tools suffer from intensive execution time and often fail on large datasets. Here, we propose SeCNV, a novel method that leverages structural entropy, to profile the copy numbers. SeCNV adopts a local Gaussian kernel to construct a matrix, depth congruent map, capturing the similarities between any two bins along the genome. Then SeCNV partitions the genome into segments by minimizing the structural entropy from the depth congruent map. With the partition, SeCNV estimates the copy numbers within each segment for cells. We simulate nine datasets with various breakpoint distributions and amplitudes of noise to benchmark SeCNV. SeCNV achieves a robust performance, i.e., the F1-scores are higher than 0.95 for breakpoint detections, significantly outperforming state-of-the-art methods. SeCNV successfully processes large datasets (>50,000 cells) within four minutes while other tools failed to finish within the time limit, i.e., 120 hours. We apply SeCNV to single-nucleus sequencing (SNS) datasets from two breast cancer patients and acoustic cell tagmentation (ACT) sequencing datasets from eight breast cancer patients. SeCNV successfully reproduces the distinct subclones and infers tumor heterogeneity. SeCNV is available at https://github.com/deepomicslab/SeCNV.

bioinformatics↗

bioRxiv2019

I-Impute: a self-consistent method to impute single cell RNA sequencing data

Single-cell RNA-sequencing (scRNA-seq) is essential for the study of cell-specific transcriptome landscapes. The scRNA-seq techniques capture merely a small fraction of the gene due to \"dropout\" events. When analyzing with scRNA-seq data, the dropout events receive intensive attentions. Imputation tools are proposed to estimate the values of the dropout events and de-noise the data. To evaluate the imputation tools, researchers have developed different clustering criteria by incorporating the ground-truth cell subgroup labels. There lack measurements without cell subgroup knowledge. A reliable imputation tool should follow the \"self-consistency\" principle; that is, the tool reports the results only if it finds no further errors or dropouts from the data. Here, we propose \"self-consistency\" as an explicit evaluation criterion; also, we propose I-Impute, a \"self-consistent\" method, to impute scRNA-seq data. I-Impute lever-ages continuous similarities and dropout probabilities and refines the data iteratively to make the final output self-consistent. On the in silico data sets, I-Impute exhibited the highest Pearson correlations for different dropout rates consistently compared with the state-of-art methods SAVER and scImpute. On the datasets of 90.87%, 70.98% and 56.65% zero rates, I-Impute exhibited the correlations as 0.78, 0.90, and 0.94, respectively, between ground truth entries and predicted values, while SAVER exhibited the correlations as 0.58, 0.79 and 0.88, respectively and scImpute exhibited correlations as 0.65, 0.86, and 0.93, respectively. Furthermore, we collected three wetlab datasets, mouse bladder cells dataset, embryonic stem cells dataset, and aortic leukocyte cells dataset, to evaluate the tools. I-Impute exhibited feasible cell subpopulation discovery efficacy on all the three datasets. It achieves the highest clustering accuracy compared with SAVER and scImpute; that is, I-Impute displayed the adjusted Rand indices of the three datasets as 0.61, 0.7, 0.52, which improved the indices of SAVER by 0.01 to 0.17, and improved the indices of scImpute by 0.19 to 0.4. Also, I-impute promoted normalized mutual information of the three datasets by 0.01 to 0.09 comparing with SAVER, and by 0.15 to 0.34 comparing with scImpute. I-Impute exhibits robust imputation ability and follows the \"self-consistency\" principle. It offers perspicacity to uncover the underlying cell subtypes in real scRNA-Seq data. Source code of I-Impute can be accessed at https://github.com/xikanfeng2/I-Impute.

bioinformatics↗