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Increasing the Content of High-Content Screening: An Overview.

Singh S, Carpenter AE, Genovesio A - J Biomol Screen (2014)

Bottom Line: This includes practical problems related to managing large and multidimensional HCS data sets as well as the adoption of assay quality statistics from HTS to HCS.Both may have led to the simplification or systematic rejection of assays carrying complex and valuable phenotypic information.We predict that advanced data analysis methods that enable full multiparametric data to be harvested for entire cell populations will enable HCS to finally reach its potential.

View Article: PubMed Central - PubMed

Affiliation: Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

No MeSH data available.


The number of papers in which a high-throughput, image-based experiment was used toward a discovery, by year of publication. Combined indicates the sum of all three searches. Note that the Combined trend line should not be considered as a total, because the literature searches are not at all comprehensive.
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fig1-1087057114528537: The number of papers in which a high-throughput, image-based experiment was used toward a discovery, by year of publication. Combined indicates the sum of all three searches. Note that the Combined trend line should not be considered as a total, because the literature searches are not at all comprehensive.

Mentions: We first wanted to observe the trend in discoveries made using HCS throughout time (here, HCS refers only to microscopy-based experiments). We used publication records as our source, which constrains our findings primarily to academia. Recognizing that it is not feasible to exhaustively identify all papers that used HCS to obtain biological results, we sought representative samplings in three ways (see the “Notes” section for details). In the first approach (termed HCS-title here), we searched PubMed for “high-content screening” (including quotes) in the title. The term HCS is by no means used universally to describe high-throughput, image-based experiments, and requiring it in the title is likely biased toward papers on the more simplistic end of the spectrum. Therefore, our second approach (termed Top-tier here) searched PubMed with a much broader combination of words and then constrained the size of this set by limiting it to papers published in Science, Nature, Cell, and the Proceedings of the National Academy of Sciences (see “Notes” for details). Our third approach (termed CellProfiler citers here) was based on a set of papers curated by hand that cite our group’s open-source software for high-throughput image analysis. We expected this group to be somewhat biased toward laboratories willing to use high-end informatics tools in their work. For all three approaches, we excluded book chapters, reviews, and comments, as well as papers in which presenting a method was the main focus (e.g., development of an assay) as opposed to presenting a biological discovery. We find that the number of papers meeting these criteria that are published each year is increasing steadily (Fig. 1).


Increasing the Content of High-Content Screening: An Overview.

Singh S, Carpenter AE, Genovesio A - J Biomol Screen (2014)

The number of papers in which a high-throughput, image-based experiment was used toward a discovery, by year of publication. Combined indicates the sum of all three searches. Note that the Combined trend line should not be considered as a total, because the literature searches are not at all comprehensive.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2 - License 3
Show All Figures
getmorefigures.php?uid=PMC4230961&req=5

fig1-1087057114528537: The number of papers in which a high-throughput, image-based experiment was used toward a discovery, by year of publication. Combined indicates the sum of all three searches. Note that the Combined trend line should not be considered as a total, because the literature searches are not at all comprehensive.
Mentions: We first wanted to observe the trend in discoveries made using HCS throughout time (here, HCS refers only to microscopy-based experiments). We used publication records as our source, which constrains our findings primarily to academia. Recognizing that it is not feasible to exhaustively identify all papers that used HCS to obtain biological results, we sought representative samplings in three ways (see the “Notes” section for details). In the first approach (termed HCS-title here), we searched PubMed for “high-content screening” (including quotes) in the title. The term HCS is by no means used universally to describe high-throughput, image-based experiments, and requiring it in the title is likely biased toward papers on the more simplistic end of the spectrum. Therefore, our second approach (termed Top-tier here) searched PubMed with a much broader combination of words and then constrained the size of this set by limiting it to papers published in Science, Nature, Cell, and the Proceedings of the National Academy of Sciences (see “Notes” for details). Our third approach (termed CellProfiler citers here) was based on a set of papers curated by hand that cite our group’s open-source software for high-throughput image analysis. We expected this group to be somewhat biased toward laboratories willing to use high-end informatics tools in their work. For all three approaches, we excluded book chapters, reviews, and comments, as well as papers in which presenting a method was the main focus (e.g., development of an assay) as opposed to presenting a biological discovery. We find that the number of papers meeting these criteria that are published each year is increasing steadily (Fig. 1).

Bottom Line: This includes practical problems related to managing large and multidimensional HCS data sets as well as the adoption of assay quality statistics from HTS to HCS.Both may have led to the simplification or systematic rejection of assays carrying complex and valuable phenotypic information.We predict that advanced data analysis methods that enable full multiparametric data to be harvested for entire cell populations will enable HCS to finally reach its potential.

View Article: PubMed Central - PubMed

Affiliation: Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

No MeSH data available.