tissue: Cultures of in vitro differentiated primary subcutaneous human adipocytes condition: LINC
Treatment protocol
Differentiated fat cells were transfected 1:3 with either our construct or an empty plasmid control using the FuGENE® HD Transfection Reagent (Promega)
Growth protocol
To induce the adipogenic conversion of adipocyte progenitors, human preadipocytes (SP-F-1, Zen-Bio, Inc.) were led to grow as a monolayer in preadipocyte medium (PM-1) until reaching confluence, and then incubated with adipocyte differentiation medium (DM-2) for 7 days. This media is composed of DMEM / Ham’s F-12 (1:1), HEPES, FBS, biotin, pantothenate, insulin, dexamethasone, IBMX, PPARγ agonist, penicillin, streptomycin and amphotericin B. Thereafter, differentiating adipocytes were maintained in adipocyte maintenance medium (AM-1) for 7 additional days (DM-2 without dexamethasone, IBMX and PPARγ agonists). During this process, the shape of preadipocytes evolves from the flattened form to rounded cells containing abundant lipid droplets, and are thus considered differentiated, mature adipocytes (~12th day and thereafter).
Extracted molecule
total RNA
Extraction protocol
Total RNA was purified from human adipose tissue and cells using RNeasy Mini Kit (QIAgen, 74104). Fat samples (~150 µg) and cell monolayers were homogenized in 0.6 mL of QIAzol® Lysis Reagent (QIAgen, 79306).
Label
Biotin
Label protocol
100ng of total RNA from each sample was processed and labelled according to manual GeneChip WT PLUS Reagent kit (P/N 703174 Rev. 2, Affymetrix Inc., Santa Clara, CA, USA)
Hybridization protocol
Sample was hybridized to Affymetrix Clariom S Human array using an Affymetrix GeneChip Hybridization Oven 645
Scan protocol
GeneChip was scanned using the Affymetrix Expression Wash, Stain and Scan User Manual (P/N 702731 Rev. 3) (Affymetrix Inc., Santa Clara, CA, USA) using Affymetrix GeneChip Scanner 3000 7G.
Description
Gene expression data from in vitro differentiated adipocytes (lincRNA overexpression during 96h)
Data processing
The data were analyzed using the RMA algorithm and then LIMMA was applied to calculate significant differential expression between samples
