Biology subjects

Bacardit, J.

Publications and source records attributed to Bacardit, J..

8 recordsLinked to original sources

bioRxiv2024

MacqD - A Deep Learning-based Model for Automatic Detection of Socially-housed Laboratory Macaques

Despite advancements in video-based behaviour analysis and detection models for various species, existing methods are suboptimal to detect macaques in complex laboratory environments. To address this gap, we present MacqD, a modified Mask R-CNN model incorporating a SWIN transformer backbone for enhanced attention-based feature extraction. MacqD robustly detects macaques in their home-cage under challenging scenarios, including occlusions, glass reflections, and overexposure to light. To evaluate MacqD and compare its performance against pre-existing macaque detection models, we collected and analysed video frames from 20 caged rhesus macaques at Newcastle University, UK. Our results demonstrate MacqDs superiority, achieving a median F1-score of 99% for frames with a single macaque in the focal cage (surpassing the next-best model by 21%) and 90% for frames with two macaques. Generalisation tests on frames from a different set of macaques from the same animal facility yielded median F1-scores of 95% for frames with a single macaque (surpassing the next-best model by 15%) and 81% for frames with two macaques (surpassing the alternative approach by 39%). Finally, MacqD was applied to videos of paired macaques from another facility and resulted in F1-score of 90%, reflecting its strong generalisation capacity. This study highlights MacqDs effectiveness in accurately detecting macaques across diverse settings.

animal behavior and cognition↗

bioRxiv2023

Reverse Engineering DNA Origami Nanostructure Designs from Raw Scaffold and Staple Sequence Lists

Designs for scaffolded DNA origami nanostructures are commonly and minimally published as the list of DNA staple and scaffold sequences required. In nearly all cases, high-level editable design files (e.g. caDNAno) which generated the low-level sequences are not made available. This de facto raw sequence exchange format allows published origami designs to be re-attempted in the laboratory by other groups, but effectively stops designs from being significantly modified or re-purposed for new future applications. To make the raw sequence exchange format more accessible to further design and engineering, in this work we propose the first algorithmic solution to the inverse problem of converting staple/scaffold sequences back to a guide schematic resembling the original origami schematic. The guide schematic can be used to aid the manual re-input of an origami into a CAD tool like caDNAno, hence recovering a high-level editable design file. Creation of a guide schematic can also be used to double check that a list of staple strand sequences does not have errors and indeed does assemble into a desired origami nanostructure prior to costly laboratory experimentation. We tested our reverse algorithm on 36 diverse origami designs from the literature and found that 29 origamis (81%) had a good quality guide schematic recovered from raw sequences. Our software is made available at https://revnano.readthedocs.io.

bioinformatics↗

bioRxiv2023

Transcriptional Biomarker Discovery Towards Building A Load Stress Reporting System for Engineered Escherichia coli Strains

Foreign proteins are produced by inserting synthetic constructs into host bacteria in biotechnology applications. This process can cause resource competition between synthetic circuits and host cells, placing a metabolic burden on the host cells which may result load stress and detrimental physiological changes. Consequently, the host bacteria can experience slow growth, while the synthetic system may suffer from suboptimal function and reduced productivity. To address this issue, we developed machine learning strategies to select a minimal number of genes that could serve as biomarkers for the design of load stress reporters. We identified pairs of biomarkers that showed discriminative capacity to detect the load stress states induced in 41 engineered E. coli strains. These biomarker genes are mainly involved in Envelope stress response, Ion transport, Energy production and conversion.

synthetic biology↗

bioRxiv2022

A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis

Recent advances in high-throughput omics technologies have enhanced the identification of molecular biomarkers specific to phenotypes or states in bacteria. Using these biomarkers to monitor the state of bacteria used in biotechnological processes promises to increase process efficiency. However, live-cell monitoring systems applied to recognise bacterial cellular states in real time can only accommodate a small number of gene expression biomarkers. Computational methods are required to identify and prioritise robust biomarkers for experimental characterisation and verification. This study focused on designing a knowledge integration strategy for the selection of an optimal minimised gene expression biomarker panel to sense various stress states in Bacillus subtilis. We developed a computational method that ranks the candidate biomarker panels based on complementary information from machine learning model, gene regulatory network and co-expression network. We identified a recommended biomarker panel showing high stress sensing power for a variety of conditions included in both the dataset used for biomarker identification (mean f1-score achieved at 0.99) and the independent datasets from different sources (mean f1-score achieved at 0.98). We discovered a significant correlation between stress sensing power and evaluation metrics such as the number of associated regulators in a B. subtilis gene regulatory network (GRN) and the number of associated modules in a B. subtilis co-expression network (CEN). GRNs and CENs provide information relevant to the diversity of biological processes encoded by biomarker genes. We demonstrate that quantitatively relating meaningful evaluation metrics with stress sensing power has potential for recognising biomarkers that show better sensitivity and robustness to an extended set of stress conditions. We conclude that this approach is readily applicable to biomarker discovery model selection.

systems biology↗

bioRxiv2021

Intrinsic and extrinsic regulation of human fetal bone marrow haematopoiesis and perturbations in Down syndrome

Throughout postnatal life, haematopoiesis in the bone marrow (BM) maintains blood and immune cell production. Haematopoiesis first emerges in human BM at 12 post conception weeks while fetal liver (FL) haematopoiesis is still expanding. Yet, almost nothing is known about how fetal BM evolves to meet the highly specialised needs of the fetus and newborn infant. Here, we detail the development of fetal BM including stroma using single cell RNA-sequencing. We find that the full blood and immune cell repertoire is established in fetal BM in a short time window of 6-7 weeks early in the second trimester. Fetal BM promotes rapid and extensive diversification of myeloid cells, with granulocytes, eosinophils and dendritic cell (DC) subsets emerging for the first time. B-lymphocyte expansion occurs, in contrast with erythroid predominance in FL at the same gestational age. We identify transcriptional and functional differences that underlie tissue-specific identity and cellular diversification in fetal BM and FL. Finally, we reveal selective disruption of B-lymphocyte, erythroid and myeloid development due to cell intrinsic differentiation bias as well as extrinsic regulation through an altered microenvironment in the fetal BM from constitutional chromosome anomaly Down syndrome during this crucial developmental time window.

developmental biology↗

bioRxiv2020

Poised cell circuits in human skin are activated in disease

The human skin confers biophysical and immunological protection through a complex cellular network that is established early in development. We profiled ~500,000 single cells using RNA-sequencing from healthy adult and developing skin, and skin from patients with atopic dermatitis and psoriasis. Our findings reveal a predominance of innate lymphoid cells and macrophages in developing skin in contrast to T cells and migratory dendritic cells in adult skin. We demonstrate dual keratinocyte differentiation trajectories and activated cellular circuits comprising vascular endothelial cells mediating immune cell trafficking, disease-specific clonally expanded IL13/IL22 and IL17A/F-expressing lymphocytes, epidermal IL23-expressing dendritic cells and inflammatory keratinocytes in disease. Our findings provide key insights into the dynamic cellular landscape of human skin in health and disease. One Sentence SummarySingle cell atlas of human skin reveals cell circuits which are quantitatively and qualitatively reconfigured in inflammatory skin disease.

immunology↗

bioRxiv2019

t-SNE transformation: a normalization method for local features of single-cell RNA-seq data

Single-cell RNA sequencing has been widely used by biology researchers. There are many analysis tools developed accordingly. However, almost all of them use log transformation in the process of normalization, which may result in system bias on global features of datasets. It is considered that they may not be suitable for researchers who expect local and detailed features of datasets, such as rare cell population and independent expressed genes. In this study, we developed a method called t-SNE transformation to replace log transformation. We found that it can well respond to some specific bio-markers in real datasets. When the cluster number was changed, t-SNE transformation was steadier than log transformation. Further study showed that clustering after t-SNE transformation detected the residual cells more accurately after majority cells of one type were removed manually. It was also sensitive to a highly-variated independent gene added artificially. In conclusion, t-SNE transformation is an alternative normalization for detecting local features, especially interests arouse in cell types with rare populations or highly-variated but independently expressed genes.

bioinformatics↗

bioRxiv2019

Decoding the development of the blood and immune systems during human fetal liver haematopoiesis

Definitive haematopoiesis in the fetal liver supports self-renewal and differentiation of haematopoietic stem cells/multipotent progenitors (HSC/MPPs), yet remains poorly defined in humans. Using single cell transcriptome profiling of ~133,000 fetal liver and ~65,000 fetal skin and kidney cells, we identify the repertoire of blood and immune cells in first and early second trimesters of development. From this data, we infer differentiation trajectories from HSC/MPPs, and evaluate the impact of tissue microenvironment on blood and immune cell development. We predict coupling of mast cell differentiation with erythro-megakaryopoiesis and identify physiological erythropoiesis in fetal skin. We demonstrate a shift in fetal liver haematopoietic composition during gestation away from being erythroid-predominant, accompanied by a parallel change in HSC/MPP differentiation potential, which we functionally validate. Our integrated map of fetal liver haematopoiesis provides a blueprint for the study of paediatric blood and immune disorders, and a valuable reference for understanding and harnessing the therapeutic potential of HSC/MPPs.

developmental biology↗