Cell-Based Screening: Extracting Meaning from Complex Data

Finkbeiner, Steven; Frumkin, Michael; Kassner, Paul D.

doi:10.1016/j.neuron.2015.02.023

Cited by 37 publications

(53 citation statements)

References 145 publications

(170 reference statements)

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“…Also, for pairwise comparisons of states such as direct comparison of the effects of two different drugs, difference spectra is a natural starting point for further analyses. In any case, the very rich data sets obtained with the described method potentially open up for a much more exploratory/data-driven approach, which can be very beneficial in this field of research due to the extreme complexity of the systems studied (Finkbeiner et al 2015).…”

Section: Discussionmentioning

confidence: 99%

Systems-level neurophysiological state characteristics for drug evaluation in an animal model of levodopa-induced dyskinesia

Tamté

Brys

Richter

et al. 2016

Journal of Neurophysiology

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Disorders affecting the central nervous system have proven particularly hard to treat, and disappointingly few novel therapies have reached the clinics in recent decades. A better understanding of the physiological processes in the brain underlying various symptoms could therefore greatly improve the rate of progress in this field. We here show how systems-level descriptions of different brain states reliably can be obtained through a newly developed method based on large-scale recordings in distributed neural networks encompassing several different brain structures. Using this technology, we characterize the neurophysiological states associated with parkinsonism and levodopa-induced dyskinesia in a rodent model of Parkinson's disease together with pharmacological interventions aimed at reducing dyskinetic symptoms. Our results show that the obtained electrophysiological data add significant information to conventional behavioral evaluations and hereby elucidate the underlying effects of treatments in greater detail. Taken together, these results potentially open up for studies of neurophysiological mechanisms underlying symptoms in a wide range of neurological and psychiatric conditions that until now have been very hard to investigate in animal models of disease.

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Section: Discussionmentioning

confidence: 99%

Systems-level neurophysiological state characteristics for drug evaluation in an animal model of levodopa-induced dyskinesia

Tamté

Brys

Richter

et al. 2016

Journal of Neurophysiology

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“…The Finkbeiner lab (Conditions B, C, D) used a Nikon Ti-E with automated ASI MS-2500 stage equipped with a spinning disc confocal microscope (Yokogawa CSU-W1), phase contrast optics (Finkbeiner et al, 2015) (Nikon S Plan Fluor 40X 0.6NA) and controlled by a custom plugin for Micro-Manager 1.4.18. An Andor Zyla4.2 camera with 2048×2048 pixels, each 6.5 μm in size, was used to generate images.…”

Section: Star Methodsmentioning

confidence: 99%

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images

Christiansen

Yang

Ando

et al. 2018

Cell

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513

433

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Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.

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“…However, in complex disease settings such as NDDs, it may be necessary for experienced biologists to determine and define novel phenotypes caused by the experimental conditions/treatments (116), and this can be challenging in large data sets. One answer is to use machine learning, in which iterative feedback can be used to hone the system’s ability to accurately identify and cluster novel, rare, or subtle phenotypes (120). The program uses information defined by the researcher’s classification of randomly chosen examples and presents a potential rule for classifying cells.…”

Section: Discussionmentioning

confidence: 99%

“…The program uses information defined by the researcher’s classification of randomly chosen examples and presents a potential rule for classifying cells. Several rounds of feedback on how rules classify a subset of the data set result in a final set of rules for each phenotype of interest that can be applied to the full data set (120). Tools such as machine learning will provide the analytical support needed to work through large complex data sets generated from in vitro drug testing and clinical trials, and they have the added potential to uncover novel or rare phenotypes undetected by humans.…”

Section: Discussionmentioning

confidence: 99%

Clinical Trials in a Dish: The Potential of Pluripotent Stem Cells to Develop Therapies for Neurodegenerative Diseases

Haston

Finkbeiner

2016

Annu. Rev. Pharmacol. Toxicol.

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Neurodegenerative diseases are a leading cause of death. No disease-modifying therapies are available, and preclinical animal model data have routinely failed to translate into success for therapeutics. Induced pluripotent stem cell (iPSC) biology holds great promise for human in vitro disease modeling because these cells can give rise to any cell in the human brain and display phenotypes specific to neurodegenerative diseases previously identified in postmortem and clinical samples. Here, we explore the potential and caveats of iPSC technology as a platform for drug development and screening, and the future potential to use large cohorts of disease-bearing iPSCs to perform clinical trials in a dish.

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Cell-Based Screening: Extracting Meaning from Complex Data

Cited by 37 publications

References 145 publications

Systems-level neurophysiological state characteristics for drug evaluation in an animal model of levodopa-induced dyskinesia

Systems-level neurophysiological state characteristics for drug evaluation in an animal model of levodopa-induced dyskinesia

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images

Clinical Trials in a Dish: The Potential of Pluripotent Stem Cells to Develop Therapies for Neurodegenerative Diseases

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