Effects of sSLAMF7 on NK cells in multiple myeloma patients

2019 Fiscal Year Research-status Report

• Project/Area Number: 19K08874
• Research Institution: Institute of Physical and Chemical Research
• Principal Investigator: ARNER ERIK 国立研究開発法人理化学研究所, 生命医科学研究センター, チームリーダー (20571839)
• Co-Investigator(Kenkyū-buntansha): 萩原 將太郎 東京女子医科大学, 医学部, 講師 (50306635)
• Project Period (FY): 2019-04-01 – 2022-03-31
• Keywords: Cell purification / Cell sample collection

Outline of Annual Research Achievements

First, in order to investigate the effect of soluble SLAMF7 on NK cells, we implemented a method of the purification of NK cells from patient peripheral blood. 1) A reagent “Isolation Cocktail and RapidSpheres purchased from STEMCELL Inc” were added to patient’s whole blood. 2) Isolation of NK cells was performed using EasySep Magnet system purchased from STEMCELL Inc. 3) The purity of NK cells was verified using Flowcytometry. After purification, the purity was 96.2%.

Second, we collect the samples of NK cells from the active multiple myeloma patients and inactive patients. We collected 12 of the active myeloma patient samples, and 4 of the inactive myeloma. Out of the 12 active multiple samples, 4 were from fresh multiple myeloma cases and 8 were from relapsed cases.

Current Status of Research Progress

3: Progress in research has been slightly delayed.

Reason

Cell sample collection and cell purification is taking slightly longer than expected. We plan to continue with the project as planned during FY2020. We expect that the collection and purification will finish during first half of FY2020, after which we can proceed with CAGE library preparation, sequencing and analysis.

Strategy for Future Research Activity

Using the CAGE data, we will attempt to reconstruct the NK cell transcriptional regulatory network (TRN) that governs multiple myeloma and is modulated by sSLAMF7.

For identifying the TRN, promoters of differentially expressed genes will be examined for the occurrence of transcription factor binding sites (TFBSs), which makes it possible to infer regulatory edges in the regulatory network. Candidate regulatory interactions will be overlaid with complementary data where possible, for example publicly available ChIP-seq data.

For validation, transcription factors will be chosen and their predicted targets will be investigated. Interactions will be assessed using siRNA knock-down of TFs followed by qRT-PCR of their putative targets.

Causes of Carryover

CAGE library preparation and sequencing is the main cost in this project. It was planned for this Fiscal Year but since the project was slightly delayed, it will instead happen next Fiscal Year.