Biology subjects

Stouffer, K.

Publications and source records attributed to Stouffer, K..

7 recordsLinked to original sources

bioRxiv2024

xIV-LDDMM Toolkit: A Suite of Image-Varifold Based Technologies for Representing and Mapping 3D Imaging and Spatial-omics Data Simultaneously Across Scales

Advancements in imaging and molecular techniques enable the collection of subcellular-scale data. Diversity in measured features, resolution, and physical scope of capture across technologies and experimental protocols pose numerous challenges to integrating data with reference coordinate systems and across scales. This paper describes a collection of technologies that we have developed for mapping data across scales and modalities, such as genes to tissues, specifically in a 3D setting. Our collection of technologies include (i) an explicit censored data representation for the partial matching problem mapping whole brains to subsampled subvolumes, (ii) a multi, scale-space optimization technology for generating resampling grids optimized to represent spatial geometry at fixed complexities, and (iii) mutual-information based functional feature selection. We integrate these technologies with our cross-modality mapping algorithm through the use of image-varifold measure norms to represent universally data across scales and imaging modalities. Collectively, these methods afford efficient representations of peta-scale imagery providing the algorithms for mapping from the nano to millimeter scales, which we term cross-modality image-varifold LDDMM (xIV-LDDMM).

bioengineering↗

bioRxiv2022

Chaotic signatures in host-microbe interactions

Host-microbe interactions constitute dynamical systems that can be represented by mathematical formulations that determine their dynamic nature, and are categorized as deterministic, stochastic, or chaotic. Knowing the type of dynamical interaction is essential for understanding the system under study. Very little experimental work has been done to determine the dynamical characteristics of host-microbe interactions and its study poses significant challenges. The most straightforward experimental outcome involves an observation of time to death upon infection. However, in measuring this outcome, the internal parameters, and the dynamics of each particular host-microbe interaction in a population of interactions are hidden from the experimentalist. To investigate whether a time-to-death (time to event) dataset provides adequate information for searching for chaotic signatures, we first determined our ability to detect chaos in simulated data sets of time-to-event measurements and successfully distinguished the time-to-event distribution of a chaotic process from a comparable stochastic one. To do so, we introduced an inversion measure to test for a chaotic signature in time-to-event distributions. Next, we searched for chaos, in time-to-death of Caenorhabditis elegans and Drosophila melanogaster infected with Pseudomonas aeruginosa or Pseudomonas entomophila, respectively. We found suggestions of chaotic signatures in both systems, but caution that our results are preliminary and highlight the need for more fine-grained and larger data sets in determining dynamical characteristics. If validated, chaos in host-microbe interactions would have important implications for the occurrence and outcome of infectious diseases, the reproducibility of experiments in the field of microbial pathogenesis and the prediction of microbial threats. ImportanceIs microbial pathogenesis a predictable scientific field? At a time when we are dealing with Coronavirus Disease 2019 (COVID-19) there is intense interest in knowing about the epidemic potential of other microbial threats and new emerging infectious diseases. To know whether microbial pathogenesis will ever be a predictable scientific field requires knowing whether a host-microbe interaction follows deterministic, stochastic, or chaotic dynamics. If randomness and chaos are absent from virulence, there is the hope for prediction in the future regarding the outcome of microbe-host interactions. Chaotic systems are inherently unpredictable although it is possible to generate shortterm probabilistic models, as is done in applications of stochastic processes and machine learning to weather forecasting. Information on the dynamics of a system is also essential for understanding the reproducibility of experiments, a topic of great concern in biological sciences. Our study finds preliminary evidence for chaotic dynamics in infectious diseases.

microbiology↗

bioRxiv2022

Amygdalar and ERC Rostral Atrophy and Tau Pathology Reconstruction in Preclinical Alzheimer's Disease

Previous research has emphasized the unique impact of Alzheimers Disease (AD) pathology on the medial temporal lobe (MTL), a reflection that tau pathology is particularly striking in the entorhinal and transentorhinal cortex (ERC, TEC) early in the course of disease. However, other brain regions are affected by AD pathology during its early phases. Here, we use longitudinal diffeomorphometry to measure the atrophy rate from MRI of the amygdala compared with that in the ERC and TEC in controls, individuals who progressed from normal cognition to mild cognitive impairment (MCI), and individuals with MCI who progressed to AD dementia, using a dataset from the Alzheimers Disease Neuroimaging Initiative (ADNI). Our results show significantly higher atrophy rates of the amygdala in both preclinical and MCI converters compared to controls, with rates of volume loss comparable to rates of thickness loss in the ERC and TEC. Using our recently developed method, referred to as Projective LDDMM, we map measures of neurofibrillary tau tangles (NFTs) from digital pathology to MRI atlases and reconstruct, for the first time, dense 3D spatial distributions of NFT density within regions of the MTL. The distribution of NFTs is consistent with the MR atrophy rates, revealing high densities not only in ERC, but in the amygdala and rostral third of the MTL. The similarity of the location of NFTs and shape changes in a well-defined clinical population suggests that amygdalar atrophy rate, as measured through MRI may be a viable biomarker for AD. PACS0000, 1111 2000 MSC0000, 1111 HighlightsO_LIAmygdala atrophy rate estimated from MRIs in preclinical Alzheimers disease (AD) C_LIO_LI3D distributions of neurofibrillary tau tangles (NFTs) reconstructed from 2D histology C_LIO_LINFTs highest in the rostral medial temporal lobe, including amygdala and ERC C_LIO_LIAmygdala atrophy rate is comparable to ERC atrophy rate in preclinical AD C_LIO_LIAmygdala and ERC atrophy rates as potential biomarkers rooted in NFT pathology C_LI

bioengineering↗

bioRxiv2022

Projective LDDMM: Mapping Molecular Digital Pathology with Tissue MRI

Reconstructing dense 3D anatomical coordinates from 2D projective measurements has become a central problem in digital pathology for both animal models and human studies. We describe a new family of diffeomorphic mapping technologies called Projective LDDMM which generate diffeomorphic mappings of dense human MRI atlases at tissue scales onto sparse measurements at micron scales associated with histological and more general optical imaging modalities. We solve the problem of dense mapping surjectively onto histological sections by incorporating new technologies for crossing modalities that use non-linear scattering transforms to represent multiple radiomic-like textures at micron scales and incorporating a Gaussian mixture-model frame-work for modelling tears and distortions associated to each section. We highlight the significance of our method through incorporation of neuropathological measures and MRI, as relevant to the development of biomarkers for Alzheimers disease and one instance of the integration of imaging data across the scales of clinical imaging and digital pathology.

bioengineering↗

bioRxiv2022

Projective LDDMM: Spatially Reconstructing a Story of Rostrally-Dominant Tau in Alzheimer's Disease

Since Braaks initial histological observations, it has been recognized that Alzheimers disease (AD) neurofibrillary tangles (NFTs) appear in the medial temporal lobe (MTL) of the brain very early in the disease course. MRI-based shape diffeomorphometry markers have demonstrated pre-clinical AD changes in the MTL but it has not been possible to confirm that these MRI changes correspond to the presence of NFTs. Here, we present a method termed Projective LDDMM for aligning sparse measurement profiles of AD pathology (i.e., 2D digital histology images) with 3D MRI. We reconstruct measures of 2D NFT density in the dense metric of 3D MRI, using the Mai Paxinos Atlas coordinates for two cases of advanced AD. Analyses reveal the highest levels of NFT density in the rostral third (10-15 mm) of the hippocampus and the adjoining regions of the entorhinal cortex and amygdala. These findings emphasize the selective vulnerability of MTL subregions in AD, and suggest that high resolution MRI methods might benefit from focusing on the rostral MTL to more closely link these MRI images to AD neuropathology.

bioengineering↗

bioRxiv2018

Human labor pain is influenced by the voltage-gated potassium channel KV6.4 subunit

AO_SCPLOWBSTRACTC_SCPLOWWe sought genetic effects on labour pain by studying healthy women who did not request analgesia during their first delivery. Extensive sensory and psychometric testing were normal in these women, except for significantly higher cuff-pressure pain. We found an excess of heterozygotes carrying the rare allele of SNP rs140124801 in KCNG4. The rare variant KV6.4-Met419 exerts a dominant negative effect and cannot modulate the voltage-dependence of KV2.1 inactivation because it fails to traffic to the plasma membrane. In vivo, we observed Kcng4 (KV6.4) expression in 40% of retrograde labelled mouse uterine sensory neurones, all of which expressed KV2.1, and over 90% expressed nociceptor genes Trpv1 and Scn10a. In neurones overexpressing KV6.4-Met419, the voltage-dependence of inactivation for KV2.1 is more depolarised compared to neurones overexpressing KV6.4. Finally, KV6.4-Met419 overexpressing neurones have a higher action potential threshold. We conclude KV6.4 can influence human labour pain by modulating the excitability of uterine nociceptors.

neuroscience↗

bioRxiv2018

Chance is an important element in phagolysosomal acidification that favors the macrophage

Microbial ingestion by a macrophage results in the formation of an acidic phagolysosome but the host cell has no information on the pH susceptibility of the ingested organism. This poses a problem for the macrophage and raises the fundamental question of how the phagocytic cell optimizes the acidification process to prevail. We analyzed the dynamical distribution of phagolysosomal pH in murine and human macrophages that had ingested live or dead Cryptococcus neoformans cells, or inert beads. Phagolysosomal acidification produced a range of pH values that approximated normal distributions, but these differed from normality depending on ingested particle type. Analysis of the increments of pH reduction revealed no forbidden ordinal patterns, implying that phagosomal acidification process was a stochastic dynamical system. Using simulation modeling, we determined that by stochastically acidifying a phagolysosome to a pH within the observed distribution, macrophages sacrificed a small amount of overall fitness to gain the benefit of reduced variation in fitness. Hence, chance in the final phagosomal pH introduces unpredictability to the outcome of the macrophage-microbe, which implies a bet-hedging strategy that benefits the macrophage. While bet hedging is common in biological systems at the organism level, our results show its use at the organelle and cellular level.

microbiology↗