Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

Melan-A Antibody, FITC conjugated


Antibody ID


Target Antigen

Melan-A human, mouse, rat

Proper Citation

(Santa Cruz Biotechnology Cat# sc-20032 FITC, RRID:AB_2713930)


monoclonal antibody


Image validation available for WB, IP in MDS.

Clone ID


Host Organism



Santa Cruz Biotechnology

Cat Num

sc-20032 FITC

Publications that use this research resource

Multi-stage Differentiation Defines Melanoma Subtypes with Differential Vulnerability to Drug-Induced Iron-Dependent Oxidative Stress.

  • Tsoi J
  • Cancer Cell
  • 2018 May 14

Literature context:


Malignant transformation can result in melanoma cells that resemble different stages of their embryonic development. Our gene expression analysis of human melanoma cell lines and patient tumors revealed that melanoma follows a two-dimensional differentiation trajectory that can be subclassified into four progressive subtypes. This differentiation model is associated with subtype-specific sensitivity to iron-dependent oxidative stress and cell death known as ferroptosis. Receptor tyrosine kinase-mediated resistance to mitogen-activated protein kinase targeted therapies and activation of the inflammatory signaling associated with immune therapy involves transitions along this differentiation trajectory, which lead to increased sensitivity to ferroptosis. Therefore, ferroptosis-inducing drugs present an orthogonal therapeutic approach to target the differentiation plasticity of melanoma cells to increase the efficacy of targeted and immune therapies.

Funding information:
  • BLRD VA - IK2 BX001559(United States)
  • NCATS NIH HHS - UL1 TR000124()
  • NCI NIH HHS - R21 CA169993()
  • NIGMS NIH HHS - T32 GM008042()

Simultaneous enumeration of cancer and immune cell types from bulk tumor gene expression data.

  • Racle J
  • Elife
  • 2017 Nov 13

Literature context:


Immune cells infiltrating tumors can have important impact on tumor progression and response to therapy. We present an efficient algorithm to simultaneously estimate the fraction of cancer and immune cell types from bulk tumor gene expression data. Our method integrates novel gene expression profiles from each major non-malignant cell type found in tumors, renormalization based on cell-type-specific mRNA content, and the ability to consider uncharacterized and possibly highly variable cell types. Feasibility is demonstrated by validation with flow cytometry, immunohistochemistry and single-cell RNA-Seq analyses of human melanoma and colorectal tumor specimens. Altogether, our work not only improves accuracy but also broadens the scope of absolute cell fraction predictions from tumor gene expression data, and provides a unique novel experimental benchmark for immunogenomics analyses in cancer research (http://epic.gfellerlab.org).