Reporting biomarkers assessed by routine immunohistochemical (IHC) staining of tissue is broadly used in diagnostic pathology laboratories for patient care. To date, clinical reporting is predominantly qualitative or semi-quantitative. By creating a multitask deep learning framework referred to as DeepLIIF, we are presenting a single step solution to nuclear segmentation and quantitative single-cell IHC scoring. Leveraging a unique de novo dataset of co-registered IHC and multiplex immunoflourescence (mpIF) data generated from the same tissue section, we simultaneously segment and translate low-cost and prevalent IHC slides to more expensive-yet-informative mpIF images. Moreover, a nuclear-pore marker, LAP2beta, is co-registered to improve cell segmentation and protein expression quantification on IHC slides.