Gloria, N. | Search bioRxiv

bioRxiv2024

Identification of Southeast Asian Anopheles mosquito species with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry using a cross-correlation approach

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is proposed for mosquito species identification. The absence of public repositories for sharing mass spectra and of open-source data analysis pipelines for fingerprint matching to mosquito species limits widespread use of this technology. The objective of this study was to develop an open-source data analysis pipeline for Anopheles species identification with MALDI-TOF MS. Malaria mosquitos were captured in 33 villages in Karen (Kayin) state in Myanmar. 359 specimens were identified with DNA barcodes and assigned to 21 sensu stricto species and 5 sibling species pairs or complexes. 3584 mass spectra of the head of these specimens identified with DNA barcoding were acquired and the similarity between mass spectra was quantified using a cross-correlation approach adapted from the published literature. A simulation experiment was carried out to evaluate the performance of species identification with MALDI-TOF MS at varying thresholds of cross-correlation index for the algorithm to output an identification result and with varying numbers of technical replicates for the tested specimens, considering PCR identification results as the reference. With one spot and a threshold value of -14 for the cross-correlation index on the log scale, the sensitivity was 0.99 (95%CrI: 0.98 to 1.00), the predictive positive value was 0.99 (95%CrI: 0.98 to 0.99) and the accuracy was 0.98 (95%CrI: 0.97 to 0.99). It was not possible to directly estimate the sensitivity and negative predictive value because there was no true negative in the assessment. In conclusion, the modified cross-correlation approach can be used for matching mass spectral fingerprints to predefined taxa and MALDI-TOF MS is a valuable tool for rapid, accurate and affordable identification of malaria mosquitos.

bioinformatics↗

bioRxiv2023

CZ CELLxGENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

Hundreds of millions of single cells have been analyzed to date using high throughput transcriptomic methods, thanks to technological advances driving the increasingly rapid generation of single-cell data. This provides an exciting opportunity for unlocking new insights into health and disease, made possible by meta-analysis that span diverse datasets building on recent advances in large language models and other machine learning approaches. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, a major challenge remains the sheer number of datasets and inconsistent format, data models and accessibility. Many datasets are available via unique portals platforms that often lack interoperability. Here, we present CZ CellxGene Discover (cellxgene.cziscience.com), a data platform that provides curated and interoperable data. This single-cell data resource, available via a free-to-use online data portal, hosts a growing corpus of community contributed data that spans more than 50 million unique cells. Curated, standardized, and associated with consistent cell-level metadata, this collection of interoperable single-cell transcriptomic data is the largest of its kind. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to rapidly explore individual datasets and perform cross-corpus analysis. This functionality is enabling meta-analyses of tens of millions of cells across studies and tissues and providing global views of human cells at the resolution of single cells.

cell biology↗