Biology subjects

Hicks, D. G.

Publications and source records attributed to Hicks, D. G..

6 recordsLinked to original sources

bioRxiv2024

Xenon and nitrous oxide induced changes in resting EEG activity can be explained by systematic increases in the relaxation rates of stochastically driven alpha band oscillatory activity

Resting electroencephalographic activity is typically indistinguishable from a filtered linear random process across a diverse range of behavioural and pharmacological states, suggesting that the power spectral density of the resting electroencephalogram (EEG) can be modelled as the superposition of multiple, stochastically driven and independent, alpha band (8 - 13 Hz) relaxation oscillators. This simple model can account for variations in alpha band power and 1/f scaling in eyes-open/eyes-closed conditions in terms of alterations in the distribution of the alpha band oscillatory relaxation rates. As changes in alpha band power and 1/f scaling have been reported in anaesthesia we hypothesise that such changes may also be accounted for by alterations in alpha band relaxation oscillatory rate distributions. On this basis we choose to study the EEG activity of xenon and nitrous oxide, gaseous anaesthetic agents that have been reported to produce different EEG effects, notable given they are both regarded as principally acting via N-methyl-D-aspartate (NMDA) receptor antagonism. By recording high density EEG from participants receiving equilibrated step-level increases in inhaled concentrations of xenon (n = 24) and nitrous oxide (n = 20), alpha band relaxation rate (damping) distributions were estimated by solving an inhomogeneous integral equation describing the linear superposition of multiple alpha-band relaxation oscillators having a continuous distribution of dampings. For both agents, level-dependent reductions in alpha band power and spectral slope exponent (15-40 Hz) were observed, that were accountable by increases in mean alpha band damping. These shared increases suggest that, consistent with their identified molecular targets of action, xenon and nitrous oxide are mechanistically similar, a conclusion further supported by neuronal population modelling in which NMDA antagonism is associated with increases in damping and reductions in peak alpha frequency. Alpha band damping may provide an important link between experiment and theories of consciousness, such as the global neuronal network theory, where the likelihood of a globally excited state ( conscious percept), is inversely related to mean damping.

neuroscience↗

bioRxiv2024

Early cytokine-driven adaptation of survival pathways in lymphoid cells during targeted therapies

Venetoclax, a first-in-class BH3 mimetic drug targeting BCL-2, has improved outcomes for patients with chronic lymphocytic leukemia (CLL). Early measurements of the depth of the venetoclax treatment response, assessed by minimal residual disease, are strong predictors of long-term clinical outcomes. Yet, there are limited data concerning the early changes induced by venetoclax treatment that might inform strategies to improve responses. To address this gap, we conducted longitudinal mass cytometric profiling of blood cells from patients with CLL during the first two months of venetoclax monotherapy. At baseline, we resolved CLL heterogeneity at the single-cell level to define multiple subpopulations in all patients distinguished by proliferative, metabolic and cell survival proteins. Venetoclax induced significant reduction in all CLL subpopulations coincident with rapid upregulation of pro-survival BCL-2, BCL-XL and MCL-1 proteins in surviving cells, which had reduced sensitivity to the drug. Mouse models recapitulated the venetoclax-induced elevation of survival proteins in B cells and CLL-like cells that persisted in vivo, with genetic models demonstrating that extensive apoptosis and access to the B cell cytokine, BAFF, were essential. Accordingly, analysis of patients with CLL that were treated with a different targeted therapy, the anti-CD20 antibody obinutuzumab, also exhibited marked elevation of BAFF and increased pro-survival proteins in leukemic cells that persisted. Overall, these data highlight the rapid adaptation of CLL cells to targeted therapies via homeostatic factors and support co-targeting of cytokine signals to achieve deeper and more durable long-term responses. Key pointsO_LILeukaemic cells rapidly adapt to targeted therapy by elevating pro-survival protein expression. C_LIO_LICell attrition and increased bioavailability of homeostatic cytokines drive this heightened survival, highlighting avenues for more potent combination therapies. C_LI

cancer biology↗

bioRxiv2024

DeepKymoTracker: A tool for accurate construction of cell lineage trees for highly motile cells

Time-lapse microscopy has long been used to record cell lineage trees. Successful construction of a lineage tree requires tracking and preserving the identity of multiple cells across many images. If a single cell is misidentified the identity of all its progeny will be corrupted and inferences about heritability may be incorrect. Successfully avoiding such identity errors is challenging, however, when studying cells such as T lymphocytes. These are highly motile and readily change shape from one image to the next. To address this problem, we developed DeepKymoTracker, a pipeline for combined tracking and segmentation. Central to DeepKymoTracker is the use of a seed, a marker which transmits information about cell position and identity between sets of images during tracking, as well as between tracking and segmentation steps. The seed allows a 3D convolutional neural network (CNN) to detect and associate cells across several consecutive images in an integrated way, reducing the risk of a single poor image corrupting cell identity. DeepKymoTracker was trained extensively on synthetic and experimental T lymphocyte images. It was benchmarked against five publicly available, automatic cell-analysis tools and outperformed them in almost all respects. The software is written in pure Python and is freely available. We suggest this tool is particularly suited to the tracking of cells in suspension, whose fast motion makes lineage assembly particularly difficult. Author SummaryCell lineage trees provide a striking visual representation of cellular decision-making and fate determination in a proliferating clone. Traditionally, these trees have been recorded using time-lapse microscopy movies of a dividing clone. Extracting lineage trees of T lymphocytes from microscopy movies is particularly challenging, however, because the cells are highly motile, readily change shape, and are easily damaged by excessive illumination and frame rates. Here we present a deep-learning approach to extracting cell lineage trees from movies of proliferating T cells. Using 3D convolutional neural networks for tracking and separate networks for segmentation we were able to reduce misidentification errors and record cell lineage trees with high fidelity. In benchmark tests, our algorithm was found to outperform all other state-of-the-art algorithms in both tracking and segmentation.

bioinformatics↗

bioRxiv2022

Cell Tree Rings: the shape of somatic evolution as a human aging timer

Biological age is typically estimated using biomarkers whose states have been observed to correlate with chronological age. A persistent limitation of such aging clocks is that it is difficult to establish how the biomarker states are related to the mechanisms of aging. Somatic mutations could potentially form the basis for a more fundamental aging clock since the mutations are both markers and drivers of aging and have a natural timescale. Cell lineage trees inferred from these mutations reflect the somatic evolutionary process and thus, it has been conjectured, the aging status of the body. Such a timer has been impractical thus far, however, because detection of somatic variants in single cells presents a significant technological challenge. Here we show that somatic mutations detected using single-cell RNA sequencing (scRNA-seq) from thousands of cells can be used to construct a cell lineage tree whose structure correlates with chronological age. De novo single-nucleotide variants (SNVs) are detected in human peripheral blood mononuclear cells using a modified protocol. A default model based on penalized multiple regression of chronological age on 31 metrics characterizing the phylogenetic tree gives a Pearson correlation of 0.81 and a median absolute error of [~]4 years between predicted and chronological age. Testing of the model on a public scRNA-seq dataset yields a Pearson correlation of 0.85. In addition, cell tree age predictions are found to be better predictors of certain clinical biomarkers than age alone, for instance glucose, albumin levels and leukocyte count. The geometry of the cell lineage tree records the structure of somatic evolution in the individual and represents a new modality of aging timer. In addition to providing a numerical estimate of Cell Tree Age, it unveils a temporal history of the aging process, revealing how clonal structure evolves over life span. Cell Tree Rings complements existing aging clocks and may help reduce the current uncertainty in the assessment of geroprotective trials.

genomics↗

bioRxiv2021

Alpha blocking and 1/fβ spectral scaling in resting EEG can be accounted for by a sum of damped alpha band oscillatory processes

The dynamical and physiological basis of alpha band activity and 1/f{beta} noise in the EEG are the subject of continued speculation. Here we conjecture, on the basis of empirical data analysis, that both of these features may be economically accounted for through a single process if the resting EEG is conceived of being the sum of multiple stochastically perturbed alpha band damped linear oscillators with a distribution of dampings (relaxation rates). The modulation of alpha-band and 1/f{beta} noise activity by changes in damping is explored in eyes closed (EC) and eyes open (EO) resting state EEG. We aim to estimate the distribution of dampings by solving an inverse problem applied to EEG power spectra. The characteristics of the damping distribution are examined across subjects, sensors and recording condition (EC/EO). We find that there are robust changes in the damping distribution between EC and EO recording conditions across participants. The estimated damping distributions are found to be predominantly bimodal, with the number and position of the modes related to the sharpness of the alpha resonance and the scaling ({beta}) of the power spectrum (1/f{beta}). The results suggest that there exists an intimate relationship between resting state alpha activity and 1/f{beta} noise with changes in both governed by changes to the damping of the underlying alpha oscillatory processes. In particular, alpha-blocking is observed to be the result of the most weakly damped distribution mode becoming more heavily damped. The results suggest a novel way of characterizing resting EEG power spectra and provides new insight into the central role that damped alpha-band activity may play in characterising the spatio-temporal features of resting state EEG. Author summaryThe resting human electroencephalogram (EEG) exhibits two dominant spectral features: the alpha rhythm (8-13 Hz) and its associated attenuation between eyes-closed and eyes-open resting state (alpha blocking), and the 1/f{beta} scaling of the power spectrum. While these phenomena are well studied a thorough understanding of their respective generative processes remains elusive. By employing a theoretical approach that follows from neural population models of EEG we demonstrate that it is possible to economically account for both of these phenomena using a singular mechanistic framework: resting EEG is assumed to arise from the summed activity of multiple uncorrelated, stochastically driven, damped alpha band linear oscillatory processes having a distribution of relaxation rates or dampings. By numerically estimating these damping distributions from eyes-closed and eyes-open EEG data, in a total of 136 participants, it is found that such damping distributions are predominantly bimodal in shape. The most weakly damped mode is found to account for alpha band power, with alpha blocking being driven by an increase in the damping of this weakly damped mode, whereas the second, and more heavily damped mode, is able to explain 1/f{beta} scaling present in the resting state EEG spectra.

neuroscience↗

bioRxiv2018

Statistical Inference in Cell Lineage Trees

New approaches to lineage tracking allow the study of cell differentiation over many generations of cells during development in multicellular organisms. Understanding the variability observed in these lineage trees requires new statistical methods. Whereas invariant cell lineages, such as that for the nematode Caenorhabditis elegans, can be described using a lineage map, defined as the fixed pattern of phenotypes overlaid onto the binary tree structure, the variability of cell lineages from higher organisms makes it impossible to draw a single lineage map. Here, we introduce lineage variability maps which describe the pattern of second-order variation throughout the lineage tree. These maps can be undirected graphs of the partial correlations between every lineal position or directed graphs showing the dynamics of bifurcated patterns in each subtree. By using the symmetry invariance of a binary tree to develop a generalized spectral analysis for cell lineages, we show how to infer these graphical models for lineages of any depth from sample sizes of only a few pedigrees. When tested on pedigrees from C. elegans expressing a marker for pharyngeal differentiation potential, the maps recover essential features of the known lineage map. When applied to highly-variable pedigrees monitoring cell size in T lymphocytes, the maps show how most of the phenotype is set by the founder naive T cell. Lineage variability maps thus elevate the concept of the lineage map to the population level, addressing questions about the potency and dynamics of cell lineages and providing a way to quantify the progressive restriction of cell fate with increasing depth in the tree.\n\nAuthor summaryMulticellular organisms develop from a single fertilized egg by sequential cell divisions. The progeny from these divisions adopt different traits that are transmitted and modified through many generations. By tracking how cell traits change with each successive cell division throughout the family, or lineage, tree, it has been possible to understand where and how these modifications are controlled at the single-cell level, thereby addressing questions about, for example, the developmental origin of tissues, the sources of differentiation in immune cells, or the relationship between primary tumors and metastases. Such lineages often show large variability, with apparently identical founder cells giving rise to different patterns of descendants. Fundamental scientific questions, such as about the range of possible cell types a cell can give rise to, are often about this variability. To characterize this variation, and thus understand the lineage at the population level, we introduce lineage variability maps. Using data from worm and mammalian cell lineages we show how these maps provide quantifiable answers to questions about any developing lineage, such as the potency of founder cells and the progressive restriction of cell fate at each stage in the tree.

bioinformatics↗