Simulating in silico cellular responses to interventions is a promising direction to accelerate high-content image-based assays, critical for advancing drug discovery and gene editing. To support this, we introduce MorphGen, a state-of-the-art diffusion-based generative model for fluorescent microscopy that enables controllable generation across multiple cell types and perturbations. To capture biologically meaningful patterns consistent with known cellular morphologies, MorphGen is trained with an alignment loss to match its representations to the phenotypic embeddings of OpenPhenom, a biological foundation model. Unlike prior approaches that compress multichannel stains into RGB images --thus sacrificing organelle-specific detail-- MorphGen generates the complete set of fluorescent channels jointly, preserving per-organelle structures. Despite modeling four cell types and generating the complete set of fluorescent channels, MorphGen achieves an FID score over 35% lower than the prior state-of-the-art MorphoDiff, which only generates RGB images for a single cell type.