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opencc-by-4.0Mar 2020View details →

Detection of HER2+ Breast Cancer Cells using Bioinspired DNA-Based Signal Amplification

Circulating tumor cells (CTC) are promising biomarkers for metastatic cancer detection and monitoring progression. However, CTC detection remains challenging due to their low frequency and heterogeneity. Herein, we report a bioinspired approach to detect individual cancer cells, based on a signal amplification cascade using a programmable DNA hybridization chain reaction (HCR) circuits. We applied this approach to detect HER2+ cancer cells using the anti-HER2 antibody (trastuzumab) coupled to initiator DNA eliciting a HCR cascade that leads to a fluorescent signal at the cell surface. At 4°C, this HCR detection scheme resulted in highly efficient, specific and sensitive signal amplification of the DNA hairpins specifically on the membrane of the HER2+ cells in a background of HER2- cells and peripheral blood leukocytes, which remained almost non-fluorescent. The results indicate that this system offers a new strategy that may be further developed toward an in vitro diagnostic platform for the sensitive and efficient detection of CTC.

opencc-by-4.0Aug 2023View details →

Identification of biomarkers for the early detection of non-small cell lung cancer: a systematic review and meta-analysis

We sought to identify the best biomarkers for the early diagnosis of LC, using a systematic review of seven databases. We identified 79 articles that focused on the identification and assessment of diagnostic biomarkers and then performed a meta-analysis. This work has been submitted for publication.

openapache2.0Jun 2019View details →

CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data: Codes and processed data

Codes and processed data to reproduce the analysis discussed in:  Wegmann et Al., CellSIUS provides sensitive and specific detection of rare cell populations from complex single cell RNA-seq data, Genome Biology 2019 (Accepted)

opencc-by-4.0Sep 2024View details →

CLASSIC and OE02 Cell Detections

This dataset consists of cell detections, taken from digital whole slide images from two datasts. The method of obtaining these cell detections as well as the sources of the datasets are described in more detail below.  For both datasets, the 2D coordinates of the nuclear centroid locations and cell features were extracted using the HeteroGenius MIM Cell-Analysis Add-On (HeteroGenius, Leeds, UK). The model used was a UNet-based cell detector and classifier trained on over 50,000 manually annotated HE-stained cells. 12 nuclear features were extracted. These consisted of length (micrometers), elongation, angle, and the probabiltiy of the clel being one of the following 9 cell types: tumour cell, lymphoycte, granulocyte, plasma cell, fibroblast, smooth muscle cell, endothelial cell, normal epithelium, or other.  One dataset consisted of cell detections from 950 haematoxylin eosin (HE) stained 3mm tissue microarray (TMA) cores from the resection specimen of gastric cancer patients from the CLASSIC trial (Noh et al., 2014). Manual annotations were made of different tissue classes for the purpose of supervised node classification using a graph neural network. These ground truth tissue classes can be found in the column "class" within the csv files. The tissue classes identified were cancer, lymphocyte aggregates, muscle, and stroma. For cells without a class, these were labelled as "notype". Ony 260 TMA cores contained these tissue annotations. A list of these files can be found in the file 'annotated_classic_cores.csv' The second dataset consisted of cell detections from 45 HE-stained endoscopic biopsies from oesophageal cancer patients from the OE02 trial (Girling et al. 2002). The ground truth target classes in this dataset were "tumour" and "not tumour". Exact annotations of the tumour areas were available from a previous study (Hale et al., 2016) and non-tumour areas were annotated manuyally for a seperate study.    Noh, S. H., Park, S. R., Yang, H.-K., Chung, H. C., Chung, I.-J., Kim, S.-W., Kim, H.-H., Choi, J.-H., Kim, H.-K., Yu, W., Lee, J. I., Shin, D. B., Ji, J., Chen, J.-S., Lim, Y., Ha, S., & Bang, Y.-J. (2014). Adjuvant capecitabine plus oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): 5-year follow-up of an open-label, randomised phase 3 trial. The Lancet Oncology, 15(12), 1389–1396. https://doi.org/10.1016/s1470-2045(14)70473-5 Girling, D. J., Bancewicz, J., Clark, P. I., Smith, D. B., Donnelly, R. J., Fayers, P. M., Weeden, S., Girling, D. J., Hutchinson, T., Harvey, A., & Lyddiard, J. (2002). Surgical resection with or without preoperative chemotherapy in oesophageal cancer: A randomised controlled trial. Lancet, 359(9319), 1727–1733. https://doi.org/10.1016/S0140-6736(02)08651-8 Hale, M. D., Nankivell, M., Hutchins, G. G., Stenning, S. P., Langley, R. E., Mueller, W., West, N. P., Wright, A. I., Treanor, D., Hewitt, L. C., Allum, W. H., Cunningham, D., Hayden, J. D., & Grabsch, H. I. (2016). Biopsy proportion of tumour predicts pathological tumour response and benefit from chemotherapy in resectable oesophageal carcinoma - Results from the UK MRC OE02 trial. Oncotarget, 7(47), 77565–77575. https://doi.org/10.18632/oncotarget.12723

opencc-by-4.0Aug 2021View details →

Dataset and Code for Manuscript "Multi-angle pulse shape detection of scattered light in flow cytometry for label-free cell cycle classification"

Dataset of measurements for cell cycle analysis with description: <ul> <li>ReadMe file with explanations on the data set and analysis</li> <li>exemplary Matlab script file for analysis</li> <li>binary data files conatining the pulse shapes in all channels</li> <li>FCS data files containing common flow cytometry parameters in each channel</li> </ul> Data on unsorted HEK cells, HEK cells sorted for cell cycle phases, and unsorted Jurkat cell are included.

opencc-by-4.0Oct 2023View details →

Large-scale annotated dataset for cochlear hair cell detection and classification

Our sense of hearing is mediated by cochlear hair cells, of which there are two types organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains 5 - 15 thousand terminally differentiated hair cells, and their survival is essential for hearing as they do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. Machine learning can be used to automate the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, rat, guinea pig, pig, primate, and human cochlear tissue, from normal conditions and following in-vivo and in-vitroototoxic drug application. The dataset includes over 107,000 hair cells which have been manually identified and annotated as either inner or outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair-cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to give other hearing research groups the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease. Associated code is provided here: https://github.com/indzhykulianlab/hcat-data

opencc-by-4.0Jul 2022View details →

DATA: Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs

This data set includes all the raw data collected for the following article: "Comparison between optical tissue clearing methods for detecting administered mesenchymal stromal cells in mouse lungs".

opencc-by-4.0Oct 2022View details →

Raw and processed cell lines (melanomaC818/melanomaMUM-2B/SK-MEL-28) data for detecting DMKN's mutations in melanoma cancer

Raw and processed cell lines (melanomaC818/melanomaMUM-2B/SK-MEL-28) data for detecting DMKN's mutations in melanoma cancer. This research was concluded that DMKN is a trigger of epithelial-mesenchymal transition-driven melanoma.

Fig. 9 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 9 Overall framework of proposed automated malaria diagnosis and species identification. CNN, Convolutional neural network; RBC, red blood cell; YOLO, You Only Look Once (model)

Fig. 8 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 8 Examples of false positive predictions by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)

Fig. 6 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 6 Comparison of detection performance by the original YOLOv4 model and the YOLOv4-RC3_4 model. Red arrows indicate cells not detected by the original YOLOv4 model, green arrows indicate the same cells detected by the YOLOv4-RC3_4 model. YOLO, You Only Look Once (model)

Fig. 3 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 3 Network structure of YOLOv4. CSP, cross-spatial connection; SPP, spatial pyramid pooling layer; PANet, Path Aggregation Network; CBM, Convolutional, Batch Normalisation, and Activation; CBL, Convolutional, Batch normalisation, and Leaky-ReLU; Conv, convolutional; Concat, concatenation

Fig. 4 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 4 Building blocks of the residual learning module. CBM, Convolutional, Batch normalisation and Mish (modules)

Fig. 5 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 5 Visual representation of the removal of residual blocks from C3 and C4 Res-block body. YOLO,You Only Look Once (model)

Fig. 1 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 1 Comparison of malaria diagnosis using deep learning CNN models and deep learning object detectors. CNN, Convolutional neural network

Fig. 2 in An optimised YOLOv4 deep learning model for efficient malarial cell detection in thin blood smear images

Fig. 2 Cropping of infected cells using the coordinates of predictions by the object detectors. RBC, Red blood cell; YOLO,You Only Look Once (model)

opencc-by-sa-4.0Mar 2018View details →

Noninvasive detection of macrophage activation with single-cell resolution through machine learning

Data related to the article "Noninvasive detection of macrophage activation with single-cell resolution through machine learning". The package contains 2 folders: - RawData:   This package contains raw data and examples of processing to extract the              variables employed to train and assess the models. - Variables: This package contains the extracted data from the various experiments              showed in the article.

opencc-by-4.0Oct 2018View details →

Supplementary data to: Detection of Neoantigen-specific T Cells Following a Personalized Vaccine in a Patient with Glioblastoma

Supplemental Data for the patient described in the manuscript: "Detection of Neoantigen-specific T Cells Following a Personalized Vaccine in a Patient with Glioblastoma". Summary of somatic variant calls from DNA whole exome, gene FPKM from RNA sequencing, and neoantigen predictions for high-affinity (ic50 <500 nM) candidates.