Biology subjects

Berra, G.

Publications and source records attributed to Berra, G..

3 recordsLinked to original sources

bioRxiv2023

Spiking neural networks provide accurate, efficient and robust models for whisker stimulus classification and allow for inter-individual generalization

With the help of high-performance computing, we benchmarked a selection of machine learning classification algorithms on the tasks of whisker stimulus detection, stimulus classification and behavior prediction based on electrophysiological recordings of layer-resolved local field potentials from the barrel cortex of awake mice. Machine learning models capable of accurately analyzing and interpreting the neuronal activity of awake animals during a behavioral experiment are promising for neural prostheses aimed at restoring a certain functionality of the brain for patients suffering from a severe brain injury. The liquid state machine, a highly efficient spiking neural network classifier that was designed for implementation on neuromorphic hardware, achieved the same level of accuracy compared to the other classifiers included in our benchmark study. Based on application scenarios related to the barrel cortex and relevant for neuroprosthetics, we show that the liquid state machine is able to find patterns in the recordings that are not only highly predictive but, more importantly, generalizable to data from individuals not used in the model training process. The generalizability of such models makes it possible to train a model on data obtained from one or more individuals without any brain lesion and transfer this model to a prosthesis required by the patient. Author SummaryA neural prosthesis is a computationally driven device that restores the functionality of a damaged brain region for locked-in patients suffering from the aftereffects of a brain injury or severe stroke. As such devices are chronically implanted, they rely on small, low-powered microchips with limited computational resources. Based on recordings describing the neural activity of awake mice, we show that spiking neural networks, which are especially designed for microchips, are able to provide accurate classification models in application scenarios relevant in neuroprosthetics. Furthermore, models were generalizable across mice, corroborating that it will be possible to train a model on recordings from healthy individuals and transfer it to the patients prosthesis.

neuroscience↗

bioRxiv2021

Interferon-stimulated and metallothionein-expressing macrophages are associated with acute and chronic allograft dysfunction after lung transplantation

Lung transplant (LT) recipients experience episodes of immune-mediated acute lung allograft dysfunction (ALAD). ALAD episodes are a risk factor for chronic lung allograft dysfunction (CLAD), the major cause of death after LT. We have applied single-cell RNA sequencing (scRNAseq) to bronchoalveolar lavage (BAL) cells from stable and ALAD patients and to cells from explanted CLAD lung tissue to determine key cellular elements in dysfunctional lung allografts, with a focus on macrophages. We identified two alveolar macrophage (AM) subsets uniquely represented in ALAD. Using pathway analysis and differentially expressed genes, we annotated these as pro-inflammatory interferon-stimulated gene (ISG) and metallothionein-mediated inflammatory (MT) AMs. Functional analysis of an independent set of AMs in vitro revealed that ALAD AMs exhibited a higher expression of CXCL10, a marker of ISG AMs, and increased secretion of pro-inflammatory cytokines compared to AMs from stable patients. Using publicly available BAL scRNAseq datasets, we found that ISG and MT AMs are associated with more severe inflammation in COVID-19 patients. Analysis of cells from four explanted CLAD lungs revealed similar macrophage populations. Using a single nucleotide variation calling algorithm, we also demonstrated contributions of donor and recipient cells to all AM subsets early post-transplant, with loss of donor-derived cells over time. Our data reveals extensive heterogeneity among lung macrophages after LT and indicates that specific sub-populations may be associated with allograft dysfunction, raising the possibility that these cells may represent important therapeutic targets.

immunology↗

bioRxiv2021

Pro-inflammatory alveolar macrophages associated with allograft dysfunction after lung transplantation

PurposeLung transplant (LT) recipients experience episodes of immune-mediated acute lung allograft dysfunction (ALAD). We have applied single-cell RNA sequencing (scRNAseq) to bronchoalveolar lavage (BAL) cells of stable and ALAD patients to determine key cellular elements in dysfunctional lung allografts. Our particular focus here is on studying alveolar macrophages (AMs) as scRNAseq enables us to elucidate their heterogeneity and possible association with ALAD where our knowledge from cytometry-based assays is very limited. MethodsFresh bronchoalveolar lavage (BAL) cells from 6 LT patients, 3 with stable lung function (3044 {+/-} 1519 cells) and 3 undergoing an episode of ALAD (2593 {+/-} 904 cells) were used for scRNAseq. R Bioconductor and Seurat were used to perform QC, dimensionality reduction, annotation, pathway analysis, and trajectory. Donor and recipient deconvolution was performed using single nucleotide variations. ResultsOur data revealed that AMs are highly heterogeneous (12 transcriptionally distinct subsets in stable). We identified two AM subsets uniquely represented in ALAD. Based on pathway analysis and the top differentially expressed genes in BAL we annotated them as pro-inflammatory interferon-stimulated genes (ISG) and metallothioneins-mediated inflammatory (MT). Pseudotime analysis suggested that ISG AMs represent an earlier stage of differentiation which may suggest them as monocyte drive macrophages. Our functional analysis on an independent set of BAL samples shows that ALAD samples have significantly higher expression of CXCL10, a marker of ISG AM, as we as higher secretion of pro-inflammatory cytokines. Single nucleotide variation calling algorithm has allowed us to identify macrophages of donor origin and demonstrated that donor AMs are lost with time post-transplant. ConclusionUsing scRNAseq, we observed AMs heterogeneity and identified specific subsets that may be associated with allograft dysfunction. Further exploration with scRNAseq will shed light on LT immunobiology and the role of AMs in allograft injury and dysfunction.

immunology↗