Limits...
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.


Percentage of papers that use only one or two measured features of the cells, by year of publication.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3-1087057114528537: Percentage of papers that use only one or two measured features of the cells, by year of publication.

Mentions: In the resulting 118 papers33–150 based on the search above, we then read the relevant portions to identify the main readout(s) of each high-throughput image-based experiment. Given the power of HCS to provide multiparametric readouts, we were surprised to find that roughly 60–80% of the papers used only one or two measured features of the cells (Fig. 2). Although measuring a single feature was by far the most common, those papers measuring two features typically used the main phenotype under study and cell count as a measure of toxicity. As we suspected, the HCS-title search yielded the highest percentage (83%) of low-content papers (1–2 features), whereas the CellProfiler citers search yielded the highest percentage (29%) of high-content papers (6+ features). Examining the results of all three searches together throughout time, we find the percentage of papers using only 1–2 features has stayed relatively steady during the past decade (Fig. 3).


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

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

Percentage of papers that use only one or two measured features of the cells, by year of publication.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig3-1087057114528537: Percentage of papers that use only one or two measured features of the cells, by year of publication.
Mentions: In the resulting 118 papers33–150 based on the search above, we then read the relevant portions to identify the main readout(s) of each high-throughput image-based experiment. Given the power of HCS to provide multiparametric readouts, we were surprised to find that roughly 60–80% of the papers used only one or two measured features of the cells (Fig. 2). Although measuring a single feature was by far the most common, those papers measuring two features typically used the main phenotype under study and cell count as a measure of toxicity. As we suspected, the HCS-title search yielded the highest percentage (83%) of low-content papers (1–2 features), whereas the CellProfiler citers search yielded the highest percentage (29%) of high-content papers (6+ features). Examining the results of all three searches together throughout time, we find the percentage of papers using only 1–2 features has stayed relatively steady during the past decade (Fig. 3).

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.