We showcase the core features of RamanSPy by analysing volumetric Raman spectroscopic data from a human leukaemia monocytic (THP-1) cell (Fig. ). The aim is to investigate the cell phenotype in a label-free manner using RS and methods from chemometrics. We load the data using builtin tools, and perform a spectral preprocessing protocol comprising spectral cropping to the fingerprint region (700-1800 cm -1 ), cosmic spike removal, denoising, baseline correction and normalisation (see SI). Using the visualisation tools in the package, we inspect data quality (Fig. ) and perform initial exploratory analysis by examining, e.g., data slices across wavenumber bands (Fig. ). The analysis proceeds to spectral unmixing based on: (i) N-FINDR for endmember detection, and (ii) fully constrained least squares (FCLS) for component quantification. This process is exploited to demix signal contributions from different cellular components and study their morphological organisation within the THP-1 cell. Following the peak assignment in , we distinguish endmember components related to lipids (band 1008 cm -1 ), nucleic acid (band 789 cm -1 ), cytoplasm (bands 1066, 1134, 1303, 1443 and 1747 cm -1 ), and the background (Fig. ). Finally, we produce fractional abundance reconstructions based on the extracted endmembers, which we can examine on a single-layer level (Fig. ) and across the entire volume (Fig. ) to localise cellular organelles within the cell.