scholarly journals Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies

2021 ◽  
Vol 17 (12) ◽  
pp. e1009626
Author(s):  
Phuc Nguyen ◽  
Sylvia Chien ◽  
Jin Dai ◽  
Raymond J. Monnat ◽  
Pamela S. Becker ◽  
...  
Keyword(s):  
Single Cell Analysis ◽  
Cell Types ◽  
Transmitted Light ◽  
Modern Biology ◽  
State Identification ◽  
Learning Platform ◽  
Dynamic Cell ◽  
Acute Myeloid Leukemia Cells ◽  
Rich Data ◽  
Phenotypic State

Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies. UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, which are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. We then analyzed movies of patient-derived acute myeloid leukemia cells, from which we identified stem-cell associated morphological states as well as the transition rates to and from these states. UPSIDE opens up the use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.

scholarly journals Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies

2021 ◽  
Author(s):  
Phuc H.B. Nguyen ◽  
Sylvia Chien ◽  
Jin Dai ◽  
Raymond J. Monnat ◽  
Pamela S Becker ◽  
...  
Keyword(s):  
Single Cell Analysis ◽  
Leukemia Cell ◽  
Cell Types ◽  
Transmitted Light ◽  
Modern Biology ◽  
State Identification ◽  
Learning Platform ◽  
Conversion Rates ◽  
Dynamic Cell ◽  
Phenotypic State

SummaryIdentification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static phenotypic readouts. Transmitted light images, on the other hand, provide direct morphological readouts and can be acquired over time to provide a rich data source for dynamic cell phenotypic state identification. Here, we describe an end-to-end deep learning platform, UPSIDE (for Unsupervised Phenotypic State IDEntification), for discovering cell states and their dynamics from transmitted light movies.UPSIDE uses the variational auto-encoder architecture to learn latent cell representations, that are then clustered for state identification, decoded for feature interpretation, and linked across movie frames for transition rate inference. Using UPSIDE, we identified distinct blood cell types in a heterogeneous dataset. From acute myeloid leukemia cell movies, we then identified stem-cell associated morphological states and their inter-conversion rates. UPSIDE opens up use of transmitted light movies for systematic exploration of cell state heterogeneity and dynamics in biology and medicine.

scholarly journals Single-cell analysis of human adipose tissue identifies depot- and disease-specific cell types

2019 ◽  
Vol 2 (1) ◽  
pp. 97-109 ◽  
Author(s):  
Jinchu Vijay ◽  
Marie-Frédérique Gauthier ◽  
Rebecca L. Biswell ◽  
Daniel A. Louiselle ◽  
Jeffrey J. Johnston ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Human Adipose Tissue ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Analysis ◽  
Disease Specific

scholarly journals Single-cell analysis revealed that IL4I1 promoted ovarian cancer progression

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Hongyu Zhao ◽  
Yu Teng ◽  
Wende Hao ◽  
Jie Li ◽  
Zhefeng Li ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Single Cell ◽  
Cancer Progression ◽  
Cox Regression ◽  
Single Cell Analysis ◽  
Cell Types ◽  
In Vitro Assays ◽  
Migration And Invasion ◽  
Dominant Type

Abstract Background Ovarian cancer was one of the leading causes of female deaths. Patients with OC were essentially incurable and portends a poor prognosis, presumably because of profound genetic heterogeneity limiting reproducible prognostic classifications. Methods We comprehensively analyzed an ovarian cancer single-cell RNA sequencing dataset, GSE118828, and identified nine major cell types. Relationship between the clusters was explored with CellPhoneDB. A malignant epithelial cluster was confirmed using pseudotime analysis, CNV and GSVA. Furthermore, we constructed the prediction model (i.e., RiskScore) consisted of 10 prognosis-specific genes from 2397 malignant epithelial genes using the LASSO Cox regression algorithm based on public datasets. Then, the prognostic value of Riskscore was assessed with Kaplan–Meier survival analysis and time-dependent ROC curves. At last, a series of in-vitro assays were conducted to explore the roles of IL4I1, an important gene in Riskscore, in OC progression. Results We found that macrophages possessed the most interaction pairs with other clusters, and M2-like TAMs were the dominant type of macrophages. C0 was identified as the malignant epithelial cluster. Patients with a lower RiskScore had a greater OS (log-rank P < 0.01). In training set, the AUC of RiskScore was 0.666, 0.743 and 0.809 in 1-year, 3-year and 5-year survival, respectively. This was also validated in another two cohorts. Moreover, downregulation of IL4I1 inhibited OC cells proliferation, migration and invasion. Conclusions Our work provide novel insights into our understanding of the heterogeneity among OCs, and would help elucidate the biology of OC and provide clinical guidance in prognosis for OC patients.

scholarly journals Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy

2018 ◽  
Author(s):  
Chawin Ounkomol ◽  
Sharmishtaa Seshamani ◽  
Mary M. Maleckar ◽  
Forrest Collman ◽  
Gregory R. Johnson
Keyword(s):  
Fluorescence Microscopy ◽  
Three Dimensional ◽  
Living Cells ◽  
Transmitted Light ◽  
Integrated Systems ◽  
Label Free ◽  
Subcellular Structure ◽  
Modern Biology ◽  
Transmitted Light Microscopy ◽  
Fluorescence Images

Understanding living cells as integrated systems, a challenge central to modern biology, is complicated by limitations of available imaging methods. While fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, slow, and damaging to cells. Here, we present a label-free method for predicting 3D fluorescence directly from transmitted light images and demonstrate that it can be used to generate multi-structure, integrated images.

scholarly journals Single-cell analysis reveals cellular heterogeneity and molecular determinants of hypothalamic leptin-receptor cells

2020 ◽  
Author(s):  
N. Kakava-Georgiadou ◽  
J.F. Severens ◽  
A.M. Jørgensen ◽  
K.M. Garner ◽  
M.C.M Luijendijk ◽  
...  
Keyword(s):  
Single Cell ◽  
Leptin Receptor ◽  
Single Cell Analysis ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Molecular Signature ◽  
Neuronal Populations ◽  
Hypothalamic Nuclei ◽  
Satiety Hormone ◽  
Multiple Cell

AbstractHypothalamic nuclei which regulate homeostatic functions express leptin receptor (LepR), the primary target of the satiety hormone leptin. Single-cell RNA sequencing (scRNA-seq) has facilitated the discovery of a variety of hypothalamic cell types. However, low abundance of LepR transcripts prevented further characterization of LepR cells. Therefore, we perform scRNA-seq on isolated LepR cells and identify eight neuronal clusters, including three uncharacterized Trh-expressing populations as well as 17 non-neuronal populations including tanycytes, oligodendrocytes and endothelial cells. Food restriction had a major impact on Agrp neurons and changed the expression of obesity-associated genes. Multiple cell clusters were enriched for GWAS signals of obesity. We further explored changes in the gene regulatory landscape of LepR cell types. We thus reveal the molecular signature of distinct populations with diverse neurochemical profiles, which will aid efforts to illuminate the multi-functional nature of leptin’s action in the hypothalamus.

scholarly journals Single cell analysis reveals immune cell–adipocyte crosstalk regulating the transcription of thermogenic adipocytes

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Prashant Rajbhandari ◽  
Douglas Arneson ◽  
Sydney K Hart ◽  
In Sook Ahn ◽  
Graciel Diamante ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Single Cell Analysis ◽  
Metabolic Response ◽  
Cell Types ◽  
Specific Cell ◽  
Beneficial Effects ◽  
Thermogenic Adipocytes ◽  
And Function

Immune cells are vital constituents of the adipose microenvironment that influence both local and systemic lipid metabolism. Mice lacking IL10 have enhanced thermogenesis, but the roles of specific cell types in the metabolic response to IL10 remain to be defined. We demonstrate here that selective loss of IL10 receptor α in adipocytes recapitulates the beneficial effects of global IL10 deletion, and that local crosstalk between IL10-producing immune cells and adipocytes is a determinant of thermogenesis and systemic energy balance. Single Nuclei Adipocyte RNA-sequencing (SNAP-seq) of subcutaneous adipose tissue defined a metabolically-active mature adipocyte subtype characterized by robust expression of genes involved in thermogenesis whose transcriptome was selectively responsive to IL10Rα deletion. Furthermore, single-cell transcriptomic analysis of adipose stromal populations identified lymphocytes as a key source of IL10 production in response to thermogenic stimuli. These findings implicate adaptive immune cell-adipocyte communication in the maintenance of adipose subtype identity and function.

scholarly journals Methods and sensors for functional genomic studies of cell-cycle transitions in single cells

2020 ◽  
Vol 52 (10) ◽  
pp. 468-477
Author(s):  
Alexander C. Zambon ◽  
Tom Hsu ◽  
Seunghee Erin Kim ◽  
Miranda Klinck ◽  
Jennifer Stowe ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Imaging System ◽  
Single Cells ◽  
Cell Types ◽  
Cell Analysis ◽  
Mcf7 Cells ◽  
Genomic Studies ◽  
Cell Subpopulations

Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1–5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.

scholarly journals ImmuneRegulation: a web-based tool for identifying human immune regulatory elements

2019 ◽  
Vol 47 (W1) ◽  
pp. W142-W150 ◽  
Author(s):  
Selim Kalayci ◽  
Myvizhi Esai Selvan ◽  
Irene Ramos ◽  
Chris Cotsapas ◽  
Eva Harris ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Web Based ◽  
Transcriptome Regulation ◽  
Rich Data ◽  
User Friendly ◽  
Gene Expression Levels

Abstract Humans vary considerably both in their baseline and activated immune phenotypes. We developed a user-friendly open-access web portal, ImmuneRegulation, that enables users to interactively explore immune regulatory elements that drive cell-type or cohort-specific gene expression levels. ImmuneRegulation currently provides the largest centrally integrated resource on human transcriptome regulation across whole blood and blood cell types, including (i) ∼43,000 genotyped individuals with associated gene expression data from ∼51,000 experiments, yielding genetic variant-gene expression associations on ∼220 million eQTLs; (ii) 14 million transcription factor (TF)-binding region hits extracted from 1945 ChIP-seq studies; and (iii) the latest GWAS catalog with 67,230 published variant-trait associations. Users can interactively explore associations between queried gene(s) and their regulators (cis-eQTLs, trans-eQTLs or TFs) across multiple cohorts and studies. These regulators may explain genotype-dependent gene expression variations and be critical in selecting the ideal cohorts or cell types for follow-up studies or in developing predictive models. Overall, ImmuneRegulation significantly lowers the barriers between complex immune regulation data and researchers who want rapid, intuitive and high-quality access to the effects of regulatory elements on gene expression in multiple studies to empower investigators in translating these rich data into biological insights and clinical applications, and is freely available at https://immuneregulation.mssm.edu.

Targeted Gene Therapy Vectors for Acute Myeloid Leukemia Cells Selected from Random AAV-Displayed Peptide Libraries.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 3176-3176
Author(s):  
Martin Trepel ◽  
Mi-Kyung Lee ◽  
Felix Kaul ◽  
Jurgen Kleinschmidt
Keyword(s):  
Gene Therapy ◽  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Cell Types ◽  
Peptide Libraries ◽  
Leukemia Cells ◽  
Cell Type ◽  
Gene Therapy Vectors ◽  
Acute Myeloid Leukemia Cells ◽  
Acute Myeloid

Abstract For acute myeloid leukemia, gene therapy may be a valuable tool to treat patients refractory to conventional chemotherapy regimens. However, there are no vectors available that sufficiently and specifically transduce this cell type. Cell type specific receptors can be used to target gene therapy vectors to the cells of interest (Trepel et al., Hum. Gene Ther., 2001). We have developed a screening system based on random peptide libraries displayed on adeno-associated virus type 2 (AAV) (Muller et al., Nat. Biotechnology, 2003). Such libraries consist of up to 108 different viruses that display a targeting peptide with random sequence in a capsid region that mediates binding of the virus particle to cell surface receptors. Targeted AAV gene therapy vectors that specifically transduce the cell types of interest can be selected from such a library. Here we report the selection of a random AAV display peptide library on a panel of acute myeloid leukemia cell lines (Kasumi-1, U937, HL 60). An X7 (X = any amino acid) oligonucleotide insert was cloned in to the AAV-library plasmid followed by the production of AAV library transfer shuttle AAV. To produce the final library, AAV producer cells were infected with shuttle AAV at an MOI of 1 to ensure encoding of displayed peptides by the packaged DNA. The target cells were then infected with the AAV library. Bound particles were internalized and amplified either by superfinfection of the cells with wild type adenovirus type 5 or by a high-sensitivity PCR-system of the random oligonucleotide followed by cloning of the insert and making a preselected library as above. Amplified clones were recovered and subjected to two more rounds of selection. Enriched peptide inserts were analyzed indirectly by DNA sequencing of recovered clones. After three rounds of selection, enrichment of a peptide motif within the selected AAV capsids was observed. The selected viruses were used for production of recombinant AAV vectors harboring a GFP reporter gene. Such recombinant targeted vectors transduced the target leukemia cells they have been selected on (e.g., Kasumi-1 cells) up to 60 times more efficient compared to a random clone of the initial, unselected library. We conclude that our novel targeting strategy can be applied to a great variety of cell types. The clones selected here may be used as valuable tools to target therapeutic genes to acute myeloid leukemia cells.

scholarly journals Chemotherapy exposure increases leukemia cell stiffness

Blood ◽  
2006 ◽  
Vol 109 (8) ◽  
pp. 3505-3508 ◽  
Author(s):  
Wilbur A. Lam ◽  
Michael J. Rosenbluth ◽  
Daniel A. Fletcher
Keyword(s):  
Cell Death ◽  
Leukemia Cell ◽  
Vascular Complications ◽  
Cell Types ◽  
Cell Stiffness ◽  
Microfluidic Channels ◽  
Cell Deformability ◽  
Force Microscopy ◽  
Atomic Force ◽  
Acute Myeloid Leukemia Cells

Abstract Deformability of blood cells is known to influence vascular flow and contribute to vascular complications. Medications for hematologic diseases have the potential to modulate these complications if they alter blood cell deformability. Here we report the effect of chemotherapy on leukemia cell mechanical properties. Acute lymphoblastic and acute myeloid leukemia cells were incubated with standard induction chemotherapy, and individual cell stiffness was tracked with atomic force microscopy. When exposed to dexamethasone or daunorubicin, leukemia cell stiffness increased by nearly 2 orders of magnitude, which decreased their passage through microfluidic channels. This stiffness increase occurred before caspase activation and peaked after completion of cell death, and the rate of stiffness increase depended on chemotherapy type. Stiffening with cell death occurred for all cell types investigated and may be due to dynamic changes in the actin cytoskeleton. These observations suggest that chemotherapy itself may increase the risk of vascular complications in acute leukemia.

Sign in / Sign up

Export Citation Format

Share Document