scholarly journals The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches

Genes ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 1098
Author(s):  
Taylor M. Weiskittel ◽  
Cristina Correia ◽  
Grace T. Yu ◽  
Choong Yong Ung ◽  
Scott H. Kaufmann ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systems Biology ◽  
Single Cell ◽  
Individualized Medicine ◽  
Learning Approaches ◽  
Imaging Analysis ◽  
Biological Mechanisms ◽  
Cell Systems ◽  
Cell Trajectory ◽  
Cell Data

Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.

scholarly journals Frontispiz: In Vivo Subcellular Mass Spectrometry Enables Proteo‐Metabolomic Single‐Cell Systems Biology in a Chordate Embryo Developing to a Normally Behaving Tadpole ( X. laevis )

2021 ◽  
Vol 133 (23) ◽  
Author(s):  
Camille Lombard‐Banek ◽  
Jie Li ◽  
Erika P. Portero ◽  
Rosemary M. Onjiko ◽  
Chase D. Singer ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Systems Biology ◽  
Single Cell ◽  
Cell Systems

scholarly journals Distinguishing different modes of growth using single-cell data

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Prathitha Kar ◽  
Sriram Tiruvadi-Krishnan ◽  
Jaana Männik ◽  
Jaan Männik ◽  
Ariel Amir
Keyword(s):  
Data Analysis ◽  
Single Cell ◽  
Statistical Methods ◽  
Synthetic Data ◽  
Cellular Growth ◽  
Biological Mechanisms ◽  
E Coli ◽  
High Throughput Data ◽  
Consistent Method ◽  
Cell Data

Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.

scholarly journals Single-cell systems biology: Probing the basic unit of information flow

2018 ◽  
Vol 8 ◽  
pp. 7-15 ◽  
Author(s):  
Simona Patange ◽  
Michelle Girvan ◽  
Daniel R. Larson
Keyword(s):  
Systems Biology ◽  
Single Cell ◽  
Information Flow ◽  
Basic Unit ◽  
Cell Systems

scholarly journals Using Flow Cytometry and Multistage Machine Learning to Discover Label-Free Signatures of Algal Lipid Accumulation

2018 ◽  
Author(s):  
Mohammad Tanhaemami ◽  
Elaheh Alizadeh ◽  
Claire Sanders ◽  
Babetta L. Marrone ◽  
Brian Munsky’
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
Lipid Accumulation ◽  
Time Course ◽  
Fluorescent Dyes ◽  
Single Cell Analysis ◽  
Learning Approaches ◽  
Label Free ◽  
Unlabeled Cell

Abstract—Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the method’s accuracy to predict lipid content in algal cells(Picochlorum soloecismus)during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.

2D Label Free Microscopy Imaging Analysis Using Machine Learning

2020 ◽  
Vol 2020 (14) ◽  
pp. 341-1-341-10
Author(s):  
Han Hu ◽  
Yang Lei ◽  
Daisy Xin ◽  
Viktor Shkolnikov ◽  
Steven Barcelo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Free Cell ◽  
Living Cells ◽  
Training Data ◽  
Learning Approaches ◽  
Label Free ◽  
Imaging Analysis ◽  
Microscopy Imaging ◽  
Limited Availability ◽  
Free Cells

Separation and isolation of living cells plays an important role in the fields of medicine and biology with label-free imaging often used for isolating cells. The analysis of label-free cell images has many challenges when examining the behavior of cells. This paper presents methods to analyze label-free cells. Many of the tools we describe are based on machine learning approaches. We also investigate ways of augmenting limited availability of training data. Our results demonstrate that our proposed methods are capable of successfully segmenting and classifying label-free cells.

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