Biology subjects

Baik, J.

Publications and source records attributed to Baik, J..

5 recordsLinked to original sources

bioRxiv2023

Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy

Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population identified in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of patient peripheral blood mononuclear cells (PBMCs) is a time-consuming and subjective process that currently relies on manual annotation by laboratory technicians. Additionally, while IF is relatively easy to apply to tissue sections, its application on PBMC smears presents challenges due to the presence of biological and technical artifacts. To address these challenges, we present a robust image analysis pipeline to automate the detection and analyses of CHCs in IF images. The pipeline incorporates quality control to optimize specimen preparation protocols and remove unwanted artifacts, leverages a {beta}-variational autoencoder (VAE) to learn meaningful latent representations of single-cell images and employs a support vector machine (SVM) classifier to achieve human-level CHC detection. We created a rigorously labeled IF CHC dataset including 9 patients and 2 disease sites with the assistance of 10 annotators to evaluate the pipeline. We examined annotator variation and bias in CHC detection and then provided guidelines to optimize the accuracy of CHC annotation. We found that all annotators agreed on CHC identification for only 65% of the cells in the dataset and had a tendency to underestimate CHC counts for regions of interest (ROI) containing relatively large amounts of cells (>50,000) when using conventional enumeration methods. On the other hand, our proposed approach is unbiased to ROI size. The SVM classifier trained on the {beta}-VAE encodings achieved an F1 score of 0.80, matching the average performance of annotators. Our pipeline enables researchers to explore the role of CHCs in cancer progression and assess their potential as a clinical biomarker for metastasis. Further, we demonstrate that the pipeline can identify discrete cellular phenotypes among PBMCs, highlighting its utility beyond CHCs.

bioinformatics↗

bioRxiv2023

DeepHeme: A generalizable, bone marrow classifierwith hematopathologist-level performance

Morphology-based classification of cells in the bone marrow aspirate (BMA) is a key step in the diagnosis and management of hematologic malignancies. However, it is time-intensive and must be performed by expert hematopathologists and laboratory professionals. We curated a large, high-quality dataset of 41,595 hematopathologist consensus-annotated single-cell images extracted from BMA whole slide images (WSIs) containing 23 morphologic classes from the clinical archives of the University of California, San Francisco. We trained a convolutional neural network, DeepHeme, to classify images in this dataset, achieving a mean area under the curve (AUC) of 0.99. DeepHeme was then externally validated on WSIs from Memorial Sloan Kettering Cancer Center, with a similar AUC of 0.98, demonstrating robust generalization. When compared to individual hematopathologists from three different top academic medical centers, the algorithm outperformed all three. Finally, DeepHeme reliably identified cell states such as mitosis, paving the way for image-based quantification of mitotic index in a cell-specific manner, which may have important clinical applications.

bioinformatics↗

bioRxiv2022

Visual Perception of 3D Space and Shape in Time - Part I: 2D Space Perception by 2D Linear Translation

Visual perception plays a critical role in navigating space and extracting useful semantic information crucial to survival. To identify distant landmarks, we constantly shift gaze vectors through saccades, while still maintaining the visual perception of stable allocentric space. How can we sustain stable allocentric space so effortlessly? To solve this question, we have developed a new concept of NHT (Neural Holography Tomography). This model states that retinotopy is invisible (not available to consciousness) and must be converted to a time code by traveling alpha brainwaves to perceive objects consciously. According to this framework, if identical alpha phases are continually assigned to a landmark, we perceive its exact and consistent allocentric location. To test this hypothesis, we designed reaction time (RT) experiments to observe evidence of the predicted space-to-time conversion. Various visual stimuli were generated at a wide range of eccentricities either on a large TV (up to 40{degrees}) or by LED strips on a hemispherical dome (up to 60{degrees}). Participants were instructed to report the observed patterns promptly under either covert (no eye movement) or overt (with eye movement) conditions. As predicted, stimuli presented at the center of fixation always produced the fastest RTs. The additional RT delay was precisely proportional to the eccentricity of the peripheral stimulus presentation. Furthermore, both covert and overt attention protocols created the same RT delays, and trajectories of saccadic eye motions were in parallel to the overt RT vs. eccentricity. These findings strongly support our NHT model, in which the observed RT-eccentricity dependence is indicative of the spatiotemporal conversion required for maintaining a stable allocentric frame of reference. That is, we perceive space by time.

neuroscience↗

bioRxiv2020

Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla

The two major stages of mammalian sleep - rapid eye movement sleep (REMs) and non-REM sleep (NREMs) - are characterized by distinct brain rhythms ranging from millisecond to minute-long (infraslow) oscillations. The mechanisms controlling transitions between sleep stages and how they are synchronized with infraslow rhythms remain poorly understood. Using opto- and chemogenetic manipulation, we show that GABAergic neurons in the dorsomedial medulla (dmM) promote the initiation and maintenance of REMs, in part through their projections to the dorsal and median raphe nuclei. Fiber photometry revealed that dmM GABAergic neurons are strongly activated during REMs. During NREMs, their activity fluctuated in close synchrony with infraslow oscillations in the spindle band of the electroencephalogram, and the phase of this rhythm modulated the latency of optogenetically induced REMs episodes. Thus, dmM inhibitory neurons powerfully promote REMs, and their slow activity fluctuations may coordinate transitions from NREMs to REMs with infraslow brain rhythms.

neuroscience↗

bioRxiv2020

Plasmacytoid dendritic cell expansion defines a distinct subset of RUNX1 mutated acute myeloid leukemia

Plasmacytoid dendritic cells (pDC) are the principal natural type I interferon producing dendritic cells. Neoplastic expansion of pDCs and pDC precursors leads to blastic plasmacytoid dendritic cell neoplasm (BPDCN) and clonal expansion of mature pDCs has been described in chronic myelomonocytic leukemia (CMML). The role of pDC expansion in acute myeloid leukemia (AML) is poorly studied. Here we characterize AML patients with pDC expansion (pDC-AML), which we observe in approximately 5% of AML. pDC-AML often possess crosslineage antigen expression and have adverse risk stratification with poor outcome. RUNX1 mutations are the most common somatic alterations in pDC-AML (>70%) and are much more common than in AML without PDC expansion. We demonstrate that pDCs are clonally related to, and originate from, leukemic blasts in pDC-AML. We further demonstrate that leukemic blasts from RUNX1-mutated AML upregulate a pDC transcriptional program, poising the cells towards pDC differentiation and expansion. Finally, tagraxofusp, a targeted therapy directed to CD123, reduces leukemic burden and eliminates pDCs in a patient-derived xenograft model. In conclusion, pDC-AML is characterized by a high frequency of RUNX1 mutations and increased expression of a pDC transcriptional program. CD123 targeting represents a potential treatment approach for pDC-AML.Competing Interest StatementConflict-of-interest disclosure: W.X. has received research support from Stemline Therapeutics. S.F.C. is a consultant for Imago Biosciences and has received honoraria from DAVA Oncology. A.D.G. served on advisory boards or as a consultant for Abbvie, Aptose, Celgene, Daiichi Sanyko, Genentech, received research funding from Abbvie, ADC Therapeutics, Aprea, AROG, Daiichi Sanyko, Pfizer, received Honoraria from Dava Oncology. R.K.R has received consulting fees from: Constellation, Incyte, Celgene, Promedior, CTI, Jazz Pharmaceuticals, Blueprint, Stemline, and research funding from Incyte, Constellation, and Stemline Therapeutics. M.S.T has received research funding from AbbVie, Cellerant, Orsenix, ADC Therapeutics, Biosight, Glycomimetics, Rafael Pharmaceuticals and Amgen. He also served on advisory Boards for AbbVie, BioLineRx, Daiichi-Sankyo, Orsenix, KAHR, Rigel, Nohla, Delta Fly Pharma, Tetraphase, Oncolyze, Jazz Pharma, Roche, Biosight and Novartis. He received royalties from UpToDate. R.L.L. is on the supervisory board of Qiagen and is a scientific advisor to Loxo (until 2019), Auron, Ajax, Mission Bio, Imago, C4 Therapeutics and Isoplexis, which each include an equity interest. He receives research support from and consulted for Celgene and Roche, he has received research support from Prelude Therapeutics, and he has consulted for Incyte, Novartis and Janssen. He has received honoraria from Lilly and Amgen for invited lectures and from Gilead for grant reviews.View Full Text

pathology↗