No abstract

Mechanical biomarkers associated with cytoskeletal structures have been reported as powerful label-free cell state identifiers. In order to measure cell mechanical properties, traditional biophysical (e.g., atomic force microscopy, micropipette aspiration, optical stretchers) and microfluidic approaches were mainly employed; however, they critically suffer from low-throughput, low-sensitivity, and/or time-consuming and labor-intensive processes, not allowing techniques to be practically used for cell biology research applications. Here, a novel inertial microfluidic cell stretcher (iMCS) capable of characterizing large populations of single-cell deformability near real-time is presented. The platform inertially controls cell positions in microchannels and deforms cells upon collision at a T-junction with large strain. The cell elongation motions are recorded, and thousands of cell deformability information is visualized near real-time similar to traditional flow cytometry. With a full automation, the entire cell mechanotyping process runs without any human intervention, realizing a user friendly and robust operation. Through iMCS, distinct cell stiffness changes in breast cancer progression and epithelial mesenchymal transition are reported, and the use of the platform for rapid cancer drug discovery is shown as well. The platform returns large populations of single-cell quantitative mechanical properties (e.g., shear modulus) on-the-fly with high statistical significances, enabling actual usages in clinical and biophysical studies.

No abstract

In article number https://doi.org/10.1002/smll.201700705, Aram J. Chung and co‐workers report a novel inertial microfluidic cell stretcher capable of characterizing large populations of single‐cell deformability near real‐time in a fully automated manner. This study opens up a new path to practically measure large populations of single‐cell quantitative mechanical properties on‐the‐fly with high statistical significances, enabling actual usage in clinical and biophysical studies.

Breast cancer is a heterogeneous disease that can be classified into several distinct molecular subtypes based on gene expression. Like mRNAs and miRNAs, long noncoding RNAs (lncRNAs) differ dramatically in expression across subtypes and can be used for classification. While there has been considerable emphasis on miRNAs, our knowledge is still lacking about the role of lncRNAs that comprise the majority of the mammalian transcriptome. Recently, the importance of lncRNAs in cancer has been highlighted by several studies. We have examined the expression profiles of >17,000 lncRNAs in a large set of breast tumors and have identified a lncRNA, AK001796, that is overexpressed in aggressive breast cancers. In particular, AK001796 is enriched in the aggressive claudin-low, HER-enriched, and luminal B subtypes. Furthermore, in four different models, we find that AK001796 is significantly upregulated in cell lines induced to undergo EMT and in putative mesenchymal-like cancer stem cells within cell lines suggesting this lncRNA as an inducer/facilitator of EMT. Similar results were obtained when a lung cancer cell line was induced to EMT through TGF beta treatment. Using cell fractionation, we have discovered that AK001796 is maintained predominantly in the nucleus. By RACE we have identified two isoforms of AK001796 in breast cancer cells that differ by the presence or absence of a 94 nucleotide intron. The short form (with intron spliced out) appears to be the variant enriched following EMT and may serve as a marker of aggressiveness. Interestingly, knockdown of AK001796 using antisense oligonucleotides lead to significantly increased apoptosis in EMT positive cell lines whereas in EMT negative cells knockdown had little effect. Preliminary studies for finding out the protein interacting partners identified some mesenchymal phenotype-associated proteins in pull-down studies using biotinylated oligos. To further investigate the molecular mechanisms regulated by AK001796 and its utility as a therapeutic target, we will determine the pathways induced by AK001796 by mapping its protein interaction network and downstream signaling pathways. These results nominate AK001796 as a promising therapeutic target in aggressive breast cancers. Citation Format: Maneesh Kumar, Rebecca Sinnott DeVaux, Julia J. Shen, Steven P. Davis, Marcel E. Dinger, John S. Mattick, Charles M. Perou, Jeffrey M. Rosen, Sendurai A. Mani, Jason I. Herschkowitz. LncRNA AK001796 as a therapeutic target in aggressive breast cancers. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 1598.