Migration Signatures Research Articles

Microfluidic platform for the quantitative analysis of leukocyte migration signatures.

Leukocyte migration into tissues is characteristic of inflammation. It is usually measured in vitro as the average displacement of populations of cells towards a chemokine gradient, not acknowledging other patterns of cell migration. Here, we designed and validated a microfluidic migration platform to simultaneously analyze four qualitative migration patterns: chemo-attraction, -repulsion, -kinesis and -inhibition, using single-cell quantitative metrics of direction, speed, persistence, and fraction of cells responding. We find that established chemokines C5a and IL-8 induce chemoattraction and repulsion in equal proportions, resulting in the dispersal of cells. These migration signatures are characterized by high persistence and speed and are independent of the chemokine dose or receptor expression. Furthermore, we find that twice as many T-lymphocytes migrate away than towards SDF-1 and their directional migration patterns are not persistent. Overall, our platform characterizes migratory signature responses and uncovers an avenue for precise characterization of leukocyte migration and therapeutic modulators.

Characterizing slope morphology using multifractal technique: a study from the western continental margin of India

Here, we present the slope configuration of the submarine gullies, ridges and the adjacent slump zone off Goa, along the western continental margin of India utilizing multibeam bathymetric and single-channel seismic data. The fluid flow migration signature in the form of pockmark seepages, traces of mud volcanoes and enhanced reflectors is observed in the area. Altogether thirty-three depth profiles from the gully, ridge and slump areas depict downslope progression in gully incision and varying gradients in the gullies (1.19–4.07°) and ridges (2.13–3.70°), whereas the profiles of the slump zone are comparatively steady (2.25–2.51°). The scatter plot of the three slope characteristics, viz., gradient, mean depth and root mean square relief, characterizes the profiles of the gullies, ridges and slump zone into three distinct clusters. Principal component analysis as well corroborates the categorization. Furthermore, a stochastic multifractal technique has been employed to understand the nature of the fine-scale seafloor processes active in the slope region. The three estimated parameters of the depth profiles, i.e., the degree of multifractality (α), sparseness (C 1) and the degree of smoothness (H), substantiate a very high degree of multifractality for all the thirty-three bathymetric profiles. Except for the five adjacent profiles (four from the slump zone and one from the ridge), the remaining twenty-eight depth profiles of the gully, ridges and slump zones show negligible difference. Based on the multifractal study, we conclude that the observed discrimination might be due to the significant interaction between the bottom currents off Goa and the varied seafloor morphological aspects with seepages and faults.

A NOTCH3 Transcriptional Module Induces Cell Motility in Neuroblastoma

Neuroblastoma is a childhood tumor of the peripheral sympathetic nervous system with an often lethal outcome due to metastatic disease. Migration and epithelial-mesenchymal transitions have been implicated in metastasis but they are hardly investigated in neuroblastoma. Cell migration of 16 neuroblastoma cell lines was quantified in Transwell migration assays. Gene expression profiling was used to derive a migration signature, which was applied to classify samples in a neuroblastoma tumor series. Differential expression of transcription factors was analyzed in the subsets. NOTCH3 was prioritized, and inducible transgene expression studies in cell lines were used to establish whether it functions as a master switch for motility. We identified a 36-gene expression signature that predicts cell migration. This signature was used to analyse expression profiles of 88 neuroblastoma tumors and identified a group with distant metastases and a poor prognosis. This group also expressed a known mesenchymal gene signature established in glioblastoma. Neuroblastomas recognized by the motility and mesenchymal signatures strongly expressed genes of the NOTCH pathway. Inducible expression of a NOTCH intracellular (NOTCH3-IC) transgene conferred a highly motile phenotype to neuroblastoma cells. NOTCH3-IC strongly induced expression of motility- and mesenchymal marker genes. Many of these genes were significantly coexpressed with NOTCH3 in neuroblastoma, as well as colon, kidney, ovary, and breast tumor series. The NOTCH3 transcription factor is a master regulator of motility in neuroblastoma. A subset of neuroblastoma with high expression of NOTCH3 and its downstream-regulated genes has mesenchymal characteristics, increased incidence of metastases, and a poor prognosis.

Abstract 1492: Systems analysis of epithelial-mesenchymal transition and cell invasion identifies functional gene signatures predictive of survival in lung cancer.

Abstract We have implemented a systems approach to identify molecular features associated with mesenchymal or epithelial phenotypes and cell behavior in 38 lung adenocarcinoma cell lines that encompassed common known genomic alterations that drive the development of lung adenocarcinoma. Cell line characterization based on morphology, invasiveness, motility and cell-cell adhesion was integrated with proteomic, gene expression, microRNA and methylation profiles. Signatures were established from differential expression of transcripts and proteins in subsets of cell lines with mesenchymal and epithelial characteristics as well as high or low invasion and motility. The mesenchymal, invasion and migration signatures had common upregulation of genes or proteins related to cytoskeletal and actin-binding proteins. Sets of 148 and 74 transcripts corresponding to proteins that were overexpressed in mesenchymal cell lines and high invasive cell lines, respectively, were highly predictive of survival in three independent lung adenocarcinoma datasets. Subsets of 39 and 21 transcripts corresponding to cytoskeletal and acting binding proteins in the mesenchymal and invasive signatures were found to also predict survival in the three independent lung adenocarcinoma datasets, pointing to an association between cytoskeletal and actin binding proteins, a mesenchymal phenotype and tumor progression in lung adenocarcinoma. Citation Format: Mark J. Schliekelman, Ayumu Taguchi, Jun Zhu, Xudong Dai, Qing Zhang, Alice Chin, Chee-Hong Wong, Hong Wang, Adi Gazdar, Ite Laird-Offringa, Muneesh Tewari, Jean Paul Thiery, Samir Hanash. Systems analysis of epithelial-mesenchymal transition and cell invasion identifies functional gene signatures predictive of survival in lung cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 1492. doi:10.1158/1538-7445.AM2013-1492

Highlights from Recent Cancer Literature

Highlights from Recent Cancer Literature

A Migration Signature and Plasma Biomarker Panel for Pancreatic Adenocarcinoma

Pancreatic ductal adenocarcinoma is a disease of extremely poor prognosis for which there are no reliable markers of asymptomatic disease. To identify pancreatic cancer biomarkers, we focused on a genomic interval proximal to the most common fragile site in the human genome, chromosome 3p12, which undergoes smoking-related breakage, loss of heterozygosity, and homozygous deletion as an early event in many epithelial tumors, including pancreatic cancers. Using a functional genomic approach, we identified a seven-gene panel (TNC, TFPI, TGFBI, SEL-1L, L1CAM, WWTR1, and CDC42BPA) that was differentially expressed across three different expression platforms, including pancreatic tumor/normal samples. In addition, Ingenuity Pathways Analysis (IPA) and literature searches indicated that this seven-gene panel functions in one network associated with cellular movement/morphology/development, indicative of a "migration signature" of the 3p pathway. We tested whether two secreted proteins from this panel, tenascin C (TNC) and tissue factor pathway inhibitor (TFPI), could serve as plasma biomarkers. Plasma ELISA assays for TFPI/TNC resulted in a combined area under the curve (AUC) of 0.88 and, with addition of CA19-9, a combined AUC for the three-gene panel (TNC/TFPI/CA19-9), of 0.99 with 100% specificity at 90% sensitivity and 97.22% sensitivity at 90% specificity. Validation studies using TFPI only in a blinded sample set increased the performance of CA19-9 from an AUC of 0.84 to 0.94 with the two-gene panel. Results identify a novel 3p pathway-associated migration signature and plasma biomarker panel that has utility for discrimination of pancreatic cancer from normal controls and promise for clinical application.

Prestack phase‐shift migration of separate offsets

Prestack phase‐shift migration is implemented by evaluating the offset‐wavenumber ([Formula: see text]) integral using the stationary‐phase method. Thus, the stationary point along [Formula: see text] must be calculated prior to applying the phase shift. This type of implementation allows for migration of separate offsets, as opposed to migration of the whole prestack data when using the original formulas. For zero‐offset data, the stationary point ([Formula: see text]) is known in advance, and, therefore, the phase‐shift migration can be implemented directly. For nonzero‐offset data, we first evaluate the [Formula: see text] that corresponds to the stationary point solution either numerically or through analytical approximations. The insensitivity of the phase to [Formula: see text] around the stationary point solution (its maximum) implies that even an imperfect [Formula: see text] obtained analytically can go a long way to getting an accurate image. In transversely isotropic media, the analytical solutions of the stationary point ([Formula: see text]) include more approximations than those corresponding to isotropic media (i.e., approximations corresponding to weaker anisotropy). Nevertheless, the resultant equations, obtained using Shanks transform and perturbation theory, produce accurate migration signatures for strong anisotropy (η ≈ 0.3) and even large offset‐to‐depth ratios (>2). The analytical solutions are particularly accurate in predicting the nonhyperbolic moveout behavior associated with anisotropic media, a key ingredient to performing an accurate nonhyperbolic moveout inversion for strongly anisotropic media. Although the prestack correction achieved using the phase‐shift method can also be obtained using a cascade of NMO correction, dip‐moveout (DMO) correction, and zero‐offset time migration, the prestack approach can handle sharper velocity models more efficiently. In addition, the resulting operator is sharper than that obtained from the DMO method. Synthetic, including the Marmousi, data applications of the proposed prestack migration demonstrate its usefulness.