
기계학습 모델을 활용하여 나이관련 황반변성 마우스 모델에서 부망막 섬유화 중증도와 강하게 연관된 주요 유전자를 예측하였다.


coremsg

나이관련-황반변성-마우스-모델에서-기계학습-기반-부망막-병변-중증도와-연관된-주요-유전자-예측


나이관련 황반변성 마우스 모델에서 기계학습 기반 부망막 병변 중증도와 연관된 주요 유전자 예측


title_rewrite


확장 지원 선택적 평면 조명 현미경(ExA-SPIM)은 센티미터 규모의 조직을 나노스케일 해상도로 이미징할 수 있는 새로운 현미경 기술이다.


Recent advances in tissue processing, labeling, and fluorescence microscopy are providing unprecedented views of the structure of cells and tissues at sub-diffraction resolutions and near single molecule sensitivity, driving discoveries in diverse fields of biology, including neuroscience. Biological tissue is organized over scales of nanometers to centimeters. Harnessing molecular imaging across intact, three-dimensional samples on this scale requires new types of microscopes with larger fields of view and working distance, as well as higher throughput. We present a new expansion-assisted selective plane illumination microscope (ExA-SPIM) with aberration-free 1×1×3 μm optical resolution over a large field of view (10.6×8.0 mm2) and working distance (35 mm) at speeds up to 946 megavoxels/sec. Combined with new tissue clearing and expansion methods, the microscope allows imaging centimeter-scale samples with 250×250×750 nm optical resolution (4× expansion), including entire mouse brains, with high contrast and without sectioning. We illustrate ExA-SPIM by reconstructing individual neurons across the mouse brain, imaging cortico-spinal neurons in the macaque motor cortex, and visualizing axons in human white matter.

### Competing Interest Statement

A.G., J.C., and K.S. have filed for a patent on aspects of the ExA-SPIM system.

Expansion-assisted selective plane illumination microscopy for nanoscale imaging of centimeter-scale tissues

확장-지원-선택적-평면-조명-현미경을-이용한-센티미터-규모-조직의-나노스케일-이미징


확장 지원 선택적 평면 조명 현미경을 이용한 센티미터 규모 조직의 나노스케일 이미징



성별 임금 격차는 단순한 차별이 아니라 생물학적 차이, 직업 선택, 보너스 구조 등 다양한 요인이 복합적으로 작용한 결과이다.


Biology, choices, and bonuses: The real reasons behind the so-called pay gap

The Pay Gap Doesn’t Add Up

성차별이-아닌-생물학적-차이와-선택-보너스-구조가-임금-격차의-실제-원인


성차별이 아닌 생물학적 차이와 선택, 보너스 구조가 임금 격차의 실제 원인



ElectroPhysiomeGAN은 C. elegans 뉴런의 막전위 반응과 정상상태 전류 프로파일로부터 Hodgkin-Huxley 뉴런 모델의 매개변수를 생성할 수 있는 새로운 심층 생성 모델이다.


Recent advances in connectomics, biophysics, and neuronal electrophysiology warrant modeling of neurons with further details in both network interaction and cellular dynamics. Such models may be referred to as ElectroPhysiome, as they incorporate the connectome and individual neuron electrophysiology to simulate neuronal activities. The nervous system of C. elegans is considered a viable framework for such ElectroPhysiome studies due to advances in connectomics of its somatic nervous system and electrophysiological recordings of neuron responses. In order to achieve a simulated ElectroPhysiome, the set of parameters involved in modeling individual neurons need to be estimated from electrophysiological recordings. Here, we address this challenge by developing a novel deep generative method called ElectroPhysiomeGAN (EP-GAN), which once trained, can instantly generate parameters associated with the Hodgkin-Huxley neuron model (HH-model) for neurons with graded potential response. The method combines Generative Adversarial Network (GAN) architecture with Recurrent Neural Network (RNN) Encoder and can generate an extensive number of parameters (>170) given the neuron’s membrane potential responses and steady-state current profiles. We validate our method by estimating HH-model parameters for 200 synthetic neurons with graded membrane potential followed by 9 experimentally recorded neurons (where 6 of them newly recorded) in the nervous system of C. elegans . Compared to other methods, EP-GAN is advantageous in both accuracy of generated parameters and inference speed. In addition, EP-GAN preserves performance when provided with incomplete membrane potential responses up to 25% and steady-state current profiles up to 75%. EP-GAN is designed to leverage the generative capability of GAN to align with the dynamical structure of HH-model, and thus able to achieve such performance.

### Competing Interest Statement

The authors have declared no competing interest.

ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses

c-elegans-신경-모델-매개변수-예측을-위한-electrophysiomegan-기록된-전기생리학적-반응으로부터-생물물리학적-뉴런-모델-매개변수-생성


C. elegans 신경 모델 매개변수 예측을 위한 ElectroPhysiomeGAN: 기록된 전기생리학적 반응으로부터 생물물리학적 뉴런 모델 매개변수 생성



DC-iDEP 기술을 활용하여 포도당 자극에 따른 인슐린 분비 소포체의 생물리학적 특성 변화를 규명하였다.


Organelle heterogeneity and inter-organelle associations within a single cell contribute to the limited sensitivity of current organelle separation techniques, thus hindering organelle subpopulation characterization. Here we use direct current insulator-based dielectrophoresis (DC-iDEP) as an unbiased separation method and demonstrate its capability by identifying distinct distribution patterns of insulin vesicles from pancreatic β-cells. A multiple voltage DC-iDEP strategy with increased range and sensitivity has been applied, and a differentiation factor (ratio of electrokinetic to dielectrophoretic mobility) has been used to characterize features of insulin vesicle distribution patterns. We observed a significant difference in the distribution pattern of insulin vesicles isolated from glucose-stimulated cells relative to unstimulated cells, in accordance with functional maturation of vesicles upon glucose stimulation, and interpret this to be indicative of high-resolution separation of vesicle subpopulation. DC-iDEP provides a path for future characterization of subtle biochemical differences of organelle subpopulations within any biological system.

### Competing Interest Statement

MAH declares a conflict of interest with CBio where he serves as the CSO and with Hayes Diagnostics, Inc. where he serves as COB, CEO & CSO. All other authors have no conflict of interest to declare.

iDEP-assisted isolation of insulin secretory vesicles

인슐린-분비-소포체의-dc-idep-기반-분리를-통한-특성-규명


인슐린 분비 소포체의 DC-iDEP 기반 분리를 통한 특성 규명



단일 분자 추적 기술을 활용하여 대규모로 단백질 동역학을 측정함으로써 약물의 작용 기전과 세포 내 상호작용을 규명할 수 있다.


The regulation of cell physiology depends largely upon interactions of functionally distinct proteins and cellular components. These interactions may be transient or long-lived, but often affect protein motion. Measurement of protein dynamics within a cellular environment, particularly while perturbing protein function with small molecules, may enable dissection of key interactions and facilitate drug discovery; however, current approaches are limited by throughput with respect to data acquisition and analysis. As a result, studies using super-resolution imaging are typically drawing conclusions from tens of cells and a few experimental conditions tested. We addressed these limitations by developing a high-throughput single-molecule tracking (htSMT) platform for pharmacologic dissection of protein dynamics in living cells at an unprecedented scale (capable of imaging > 106 cells/day and screening > 104 compounds). We applied htSMT to measure the cellular dynamics of fluorescently tagged estrogen receptor (ER) and screened a diverse library to identify small molecules that perturbed ER function in real time. With this one experimental modality, we determined the potency, pathway selectivity, target engagement, and mechanism of action for identified hits. Kinetic htSMT experiments were capable of distinguishing between on-target and on-pathway modulators of ER signaling. Integrated pathway analysis recapitulated the network of known ER interaction partners and suggested potentially novel, kinase-mediated regulatory mechanisms. The sensitivity of htSMT revealed a new correlation between ER dynamics and the ability of ER antagonists to suppress cancer cell growth. Therefore, measuring protein motion at scale is a powerful method to investigate dynamic interactions among proteins and may facilitate the identification and characterization of novel therapeutics.

### Competing Interest Statement

The authors are employees and/or shareholders of Eikon Therapeutics

A high-throughput platform for single-molecule tracking identifies drug interaction and cellular mechanisms

대규모-단일-분자-추적을-통한-약물-상호작용-및-세포-기전-규명


대규모 단일 분자 추적을 통한 약물 상호작용 및 세포 기전 규명



만성 알코올 섭취와 단기 금주가 혈액 대사체 프로파일에 미치는 영향을 분석하고, 이러한 대사체가 불안, 우울, 알코올 갈망과 같은 심리적 증상과 관련되어 있음을 확인하였다. 이를 통해 새로운 신경활성 대사체를 발견하여 알코올 사용 장애의 관리를 위한 잠재적 타깃으로 제안하였다.


Alcohol use disorder (AUD) is a global health problem with limited therapeutic options. The biochemical mechanisms that lead to alcohol addiction are not yet fully understood, and in this respect, metabolomics represents a promising approach to decipher metabolic events related to AUD. The plasma metabolome contains a plethora of bioactive molecules that reflects the functional changes in host metabolism but also the impact of the gut microbiome and nutritional habits. In this study, we investigated the impact of chronic alcohol abuse, and of a three-week period of alcohol abstinence, on the blood metabolome (non-targeted LC-MS metabolomics analysis) in 96 patients diagnosed with severe alcohol use disorder (AUD). We found that the plasma levels of different lipids ((lyso)phosphatidylcholines, long-chain fatty acids), short-chain fatty acids (i.e. 3-hydroxyvaleric acid) and bile acids were altered in AUD patients. In addition, several microbial metabolites, including indole-3-propionic acid, p-cresol sulfate, hippuric acid, pyrocatechol sulfate, and metabolites belonging to xanthine class (paraxanthine, theobromine and theophylline) were sensitive to alcohol abuse and alcohol withdrawal. 3-Hydroxyvaleric acid, caffeine metabolites (theobromine, paraxanthine and theophylline) and microbial metabolites (hippuric acid and pyrocatechol sulfate) were correlated with anxiety, depression and alcohol craving. Metabolomics analysis in post-mortem samples of frontal cortex and cerebrospinal fluid of those consuming a high level of alcohol revealed that those metabolites can be found also in brain tissue. Our data allow to for the identification of neuroactive metabolites, from interactions between food components and microbiota, which may represent new targets in the management of neuropsychiatric diseases such as AUD.

The study was registered at [clinicaltrial.gov][1] under the identification number [NCT03803709][2].

### Competing Interest Statement

OK and KH are founders of Afekta Technologies Ltd. The other authors report no financial interests or potential conflicts of interest.

 [1]: http://clinicaltrial.gov
 [2]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT03803709&atom=%2Fbiorxiv%2Fearly%2F2024%2F02%2F29%2F2024.02.27.582239.atom

Blood metabolomic profiling reveals new targets in the management of psychological symptoms associated with alcohol use disorder

만성-알코올-섭취가-혈액-대사체에-미치는-영향과-알코올-금단-기간-동안의-변화


만성 알코올 섭취가 혈액 대사체에 미치는 영향과 알코올 금단 기간 동안의 변화



DIETS 기법을 통해 초파리의 고체 식이 섭취량을 직접 측정하고 장기간 추적할 수 있으며, 이를 통해 다양한 식이 조건에 따른 초파리의 생리적 변화를 연구할 수 있다.


The fruit fly Drosophila melanogaste r offers a powerful model to study how diet affects the body and brain. However, existing methods for measuring their food intake often rely on dyes or tags mixed with food, which can be inaccurate due to how the flies absorb and eliminate them. Capillary-based assays like CAFE directly measure consumption, but only work with liquids and shorten fly lifespan. Additionally, capillary assays are incompatible with delivering viscous foods like high-fat diets. Even solidified high-fat diets tend to be sticky death traps for flies. Another longstanding challenge for fly researchers is that dietary restriction in flies involves diluting food, leading to compensatory feeding. To address these shortcomings, we have developed DIETS, a sensitive feeding assay that can be implemented even in low-resource settings. DIETS eliminates the need for labels and directly weighs the solid food consumed by small groups of flies over extended periods of hours to weeks. It allows us to deliver precise amounts of food to flies and implement accurate dietary restrictions. Importantly, DIETS is compatible with studying energy-dense high-fat diets. Using DIETS, we observed that, unlike a high-sugar diet, an isocaloric high-fat diet did not improve the flies’ ability to withstand starvation, even though they consumed more calories and had higher fat deposition.

### Competing Interest Statement

The authors have declared no competing interest.

DIETS: a simple and sensitive assay to measure and control the intake of complex solid foods, like high-fat diets, in Drosophila

고체-식이-섭취량-직접-측정-및-장기-추적-diets-초파리에서의-활용


고체 식이 섭취량 직접 측정 및 장기 추적(DIETS): 초파리에서의 활용



생물학적 경로 토큰화와 메모리 효율적인 다중 모달 Transformer를 통해 조직학 및 전사체 데이터의 상호작용을 모델링하여 환자 생존 예측 성능을 향상시킬 수 있다.


생존-예측을-위한-생물학적-경로와-조직학-간의-밀접한-다중-모달-상호작용-모델링


생존 예측을 위한 생물학적 경로와 조직학 간의 밀접한 다중 모달 상호작용 모델링
