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Items: 1 to 20 of 41

1.

Single cell dynamic phenotyping.

Patsch K, Chiu CL, Engeln M, Agus DB, Mallick P, Mumenthaler SM, Ruderman D.

Sci Rep. 2016 Oct 6;6:34785. doi: 10.1038/srep34785.

2.

Dichotomy of cellular inhibition by small-molecule inhibitors revealed by single-cell analysis.

Vogel RM, Erez A, Altan-Bonnet G.

Nat Commun. 2016 Sep 30;7:12428. doi: 10.1038/ncomms12428.

3.

Visualizing quantitative microscopy data: History and challenges.

Sailem HZ, Cooper S, Bakal C.

Crit Rev Biochem Mol Biol. 2016;51(2):96-101. doi: 10.3109/10409238.2016.1146222. Review.

4.

In Vivo Autofluorescence Imaging of Tumor Heterogeneity in Response to Treatment.

Shah AT, Diggins KE, Walsh AJ, Irish JM, Skala MC.

Neoplasia. 2015 Dec;17(12):862-70. doi: 10.1016/j.neo.2015.11.006.

5.

Evolution of cellular morpho-phenotypes in cancer metastasis.

Wu PH, Phillip JM, Khatau SB, Chen WC, Stirman J, Rosseel S, Tschudi K, Van Patten J, Wong M, Gupta S, Baras AS, Leek JT, Maitra A, Wirtz D.

Sci Rep. 2015 Dec 17;5:18437. doi: 10.1038/srep18437.

6.

Addressing biological uncertainties in engineering gene circuits.

Zhang C, Tsoi R, You L.

Integr Biol (Camb). 2016 Apr 18;8(4):456-64. doi: 10.1039/c5ib00275c. Review.

7.

Time series modeling of live-cell shape dynamics for image-based phenotypic profiling.

Gordonov S, Hwang MK, Wells A, Gertler FB, Lauffenburger DA, Bathe M.

Integr Biol (Camb). 2016 Jan;8(1):73-90. doi: 10.1039/c5ib00283d.

8.

A metric and workflow for quality control in the analysis of heterogeneity in phenotypic profiles and screens.

Gough A, Shun TY, Taylor DL, Schurdak M.

Methods. 2016 Mar 1;96:12-26. doi: 10.1016/j.ymeth.2015.10.007.

9.

Morphological Profiles of RNAi-Induced Gene Knockdown Are Highly Reproducible but Dominated by Seed Effects.

Singh S, Wu X, Ljosa V, Bray MA, Piccioni F, Root DE, Doench JG, Boehm JS, Carpenter AE.

PLoS One. 2015 Jul 21;10(7):e0131370. doi: 10.1371/journal.pone.0131370.

10.

On comparing heterogeneity across biomarkers.

Steininger RJ 3rd, Rajaram S, Girard L, Minna JD, Wu LF, Altschuler SJ.

Cytometry A. 2015 Jun;87(6):558-67. doi: 10.1002/cyto.a.22599.

11.

Stochastic sensitivity analysis and kernel inference via distributional data.

Li B, You L.

Biophys J. 2014 Sep 2;107(5):1247-55. doi: 10.1016/j.bpj.2014.07.025.

12.

Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery.

Gough AH, Chen N, Shun TY, Lezon TR, Boltz RC, Reese CE, Wagner J, Vernetti LA, Grandis JR, Lee AV, Stern AM, Schurdak ME, Taylor DL.

PLoS One. 2014 Jul 18;9(7):e102678. doi: 10.1371/journal.pone.0102678. Erratum in: PLoS One. 2015;10(3):e0120468.

13.

Single-cell RNA-seq reveals dynamic paracrine control of cellular variation.

Shalek AK, Satija R, Shuga J, Trombetta JJ, Gennert D, Lu D, Chen P, Gertner RS, Gaublomme JT, Yosef N, Schwartz S, Fowler B, Weaver S, Wang J, Wang X, Ding R, Raychowdhury R, Friedman N, Hacohen N, Park H, May AP, Regev A.

Nature. 2014 Jun 19;510(7505):363-9. doi: 10.1038/nature13437.

14.

Focal activation of cells by plasmon resonance assisted optical injection of signaling molecules.

Orsinger GV, Williams JD, Romanowski M.

ACS Nano. 2014 Jun 24;8(6):6151-62. doi: 10.1021/nn5015903.

15.

Quantitative characterization of cellular membrane-receptor heterogeneity through statistical and computational modeling.

Weddell JC, Imoukhuede PI.

PLoS One. 2014 May 14;9(5):e97271. doi: 10.1371/journal.pone.0097271. Erratum in: PLoS One. 2014;9(8):e107095.

16.

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

Singh S, Carpenter AE, Genovesio A.

J Biomol Screen. 2014 Jun;19(5):640-50. doi: 10.1177/1087057114528537. Review.

17.

Beyond genetics in personalized cancer treatment: assessing dynamics and heterogeneity of tumor responses.

Tyson DR, Quaranta V.

Per Med. 2013 May 1;10(3):221-225. No abstract available.

18.

Rapid analysis and exploration of fluorescence microscopy images.

Pavie B, Rajaram S, Ouyang A, Altschuler JM, Steininger RJ 3rd, Wu LF, Altschuler SJ.

J Vis Exp. 2014 Mar 19;(85). doi: 10.3791/51280.

19.

Exploiting cell-to-cell variability to detect cellular perturbations.

Dey G, Gupta GD, Ramalingam B, Sathe M, Mayor S, Thattai M.

PLoS One. 2014 Mar 4;9(3):e90540. doi: 10.1371/journal.pone.0090540.

20.

Xenopus embryonic epidermis as a mucociliary cellular ecosystem to assess the effect of sex hormones in a non-reproductive context.

Castillo-Briceno P, Kodjabachian L.

Front Zool. 2014 Feb 6;11(1):9. doi: 10.1186/1742-9994-11-9.

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