![]() ![]() However, numerically rare behaviours may have a disproportionate contribution phenotypically, such as in the case of rare populations that may be metastasis-competent, drug-resistant, or possess stem-like capabilities. It is the numerically dominant phenotype that is most often quantified when using basic 3D culture analyses with low sample numbers. For example, a dominant phenotype - defined by high-frequency occurrence of a particular sequence of cell state changes in a cell population - may occur simultaneously with a less-frequent alternate phenotype. These programmes may not occur at equal frequencies. Heterogeneity may represent modest variation in a singular morphogenesis pattern or distinct biological programmes that occur in parallel to result in alternate phenotypes. Whether 3D phenotypes are largely homogeneous or the extent to which heterogeneity exists in 3D culture is a poorly investigated area. This is a significant bottleneck in realising the potential of 3D culture to identify the extent, repertoire, and biological consequences of heterogeneity. Such simple analyses reflect the combination of increased cost and complexity of sampling 3D volumes over time compared to sampling 2-dimensional (2D) cell populations and a lack of analysis tools for the resulting large datasets. Despite the power of such approaches, most other studies typically rely on the averaging of coarse features, such as size, viability or sphericity, to define changes occurring in response to a treatment. Static fluorescent imaging of 3D organoids to couple cell morphological features with the spatial distribution of fate or signalling markers has been elegantly used to predict how cell fate changes underpin alternate phenotypes 15, 16. The 3-Dimensional (3D) culture of cells or tissue fragments to induce complex multicellular structures, such as cysts, acini, spheroids or organoids, allows in vitro determination of how alternate cell states cooperate to give rise to a phenotype. While powerful, these methods are defined by terminal snapshots, which fail to capture the dynamics of how cell states changing over time is a defining feature of morphogenesis. Several computational approaches use static time points to predict the sequence in which alternate cell states occur to give rise to alternate phenotypes 8, 9, 10, 11, 12, 13, 14. Markers of distinct cell states, be they genetic, proteomic or morphological features, can be extrapolated to infer cell function 1, 2, 3, 4, 5, 6, 7. The profiling of cell populations at the single-cell level has transformed quantitative cell biology and unlocked the potential to understand heterogeneous cell states. Traject3d is therefore an important companion to other single-cell technologies by facilitating real-time identification via live imaging of how distinct states can lead to alternate phenotypes that occur in parallel in 3D culture. We use this to characterise the temporal landscape of morphological states of cancer cell lines, varying in metastatic potential and drug resistance, and use this information to identify drug combinations that inhibit such heterogeneity. Here we develop Traject3d (Trajectory identification in 3D), a method for identifying heterogeneous states in 3D culture and how these give rise to distinct phenotypes over time, from label-free multi-day time-lapse imaging. Whether cells plated into 3D cultures give rise to a singular phenotype or whether multiple biologically distinct phenotypes arise in parallel is largely unknown due to a lack of tools to detect such heterogeneity. The 3-dimensional (3D) culture of cells or tissue fragments provides a system to study how such states contribute to multicellular morphogenesis. Single cell profiling by genetic, proteomic and imaging methods has expanded the ability to identify programmes regulating distinct cell states. ![]() Nature Communications volume 13, Article number: 5317 ( 2022) Traject3d allows label-free identification of distinct co-occurring phenotypes within 3D culture by live imaging ![]()
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