Proteome data associated with the leaf senescence in Glycine max

The data presented in this article are associated with the article “Coupling of gel-based 2-DE and 1-DE shotgun proteomics approaches to dig deep into the leaf senescence proteome of Glycine max” (R. Gupta, S.J. Lee, C.W. Min, S.W. Kim, K.-H. Park, D.-W. Bae, et al., 2016) [1]. Leaf senescence is one of the important aspects of the life cycle of a plant that leads to the recycling of nutrients from source to sink cells. To understand the leaf senescence-associated proteins, we used a combination of gel-based 2-DE and 1-DE shotgun proteomic approaches. Here, we display the 2-DE, Mass spectrometry, and Gene ontology data related with the leaf senescence in soybean [1].


a b s t r a c t
The data presented in this article are associated with the article "Coupling of gel-based 2-DE and 1-DE shotgun proteomics approaches to dig deep into the leaf senescence proteome of Glycine max" (R. Gupta [1]. Leaf senescence is one of the important aspects of the life cycle of a plant that leads to the recycling of nutrients from source to sink cells. To understand the leaf senescence-associated proteins, we used a combination of gelbased 2-DE and 1-DE shotgun proteomic approaches. Here, we display the 2-DE, Mass spectrometry, and Gene ontology data related with the leaf senescence in soybean [1]. & Value of the data 1. This data set depicts the comparative proteome analysis between two contrasting stages of leaf development, R3 (mature leaf) and R7 (senescent leaf). 2. A total of 1234 proteins were identified from R3 and R7 leaves using a combination of 2-DE and shot-gun proteomic approaches. 3. Data reported here deepen our understanding on leaf senescence at proteome level and could be used to develop senescence specific biomarker(s) in future.  Tables 5 and 6) approaches, from PEG -supernatant and -pellet fractions of R3 and R7 leaves. Detailed description of the data and methods is reported previously [1].

Protein isolation
For the identification of senescence-associated proteins, first, total leaf proteins were extracted in 10 mL of Tris-Mg-NP-40 buffer (0.5 M Tris-HCl, pH 8.3, 2% v/v NP-40, 20 mM MgCl 2 ) followed by their precipitation using 15% PEG, as described previously [1]. Proteins from PEG-supernatant and -pellet fractions were then recovered using methanolic-phenol ammonium acetate precipitation method.

Two-dimensional gel electrophoresis, MALDI-TOF/TOF MS and shotgun proteomic analysis
Two-dimensional gel electrophoresis (2-DE) and MALDI-TOF/TOF MS identification were carried out as described in detail previously [1,2]. In brief, protein pellets after methanolic-phenol ammonium acetate precipitation were dissolved in rehydration buffer containing 7M Urea, 2M thiourea, 4% v/v CHAPS, 2M DTT, and 0.5% v/v IPG buffer pH 4-7 (GE Healthcare, Waukesha, WI, USA) and quantified by 2D-Quant kit (GE Healthcare). A total of 600 mg of proteins from three biological replicates of PEG-supernatant and -pellet fractions of R3 and R7 leaves, were loaded on the 24 cm IPG strips (pH 4-7) and first and second dimension separation of proteins was carried out as described previously [1]. Colloidal Coomassie Brilliant Blue (CBB) stained gels were scanned using a transmissive scanner (PowerLook 1120, UMAX) and analyzed by ImageMaster2DPlatinum software (ver. 6.0, GE Healthcare). Student's t-test was used to determine the statistically significant differentially modulated spots between the 2-DE gels of R3 and R7 leaves (po 0.05) [1]. Spots showing differential modulation were excised from the gels, destained, subjected to in-gel digestion and identified by a MALDI-TOF/TOF-MS (ABI 4800, Applied Biosystems, Framingham, MA, USA) as described in detail   Fig. 3. GO analysis of the identified differentially modulated spots from the 2-D gels of R3 and R7 leaves. previously [1,3]. Shotgun proteomic analysis of R3 and R7 leaf proteins was carried out using QExactive TM Orbitrap High-Resolution Mass Spectrometer as described previously [1].

Statistical analysis and functional annotation of the identified proteins
Identified proteins were functionally annotated using Gene Ontology (GO) database. Correlation matrix and dendrograms were generated using the percentage volumes of differential protein spots of supernatant and pellet fractions from three biological replicates using NIA array software [4].

Acknowledgments
This study was financially supported by a grant from SSAC (Grant no. PJ011070032016) provided to STK.

Transparency document. Supporting information
Transparency data associated with this article can be found in the online version at http://dx.doi. org/10.1016/j.dib.2016.08.045.