Latin Hypercube Sampling (LHS) is a prominent tool in simulation design, with a variety of applications in high-dimensional and computationally expensive problems. LHS allows for various optimization strategies, most notably to ensure space-filling properties. However, LHS is a single-stage algorithm that requires a priori knowledge of the targeted sample size. In this work, we present LHS in LHS, a new expansion algorithm for LHS that enables the addition of new samples to an existing LHS-distributed set while (approximately) preserving its properties. In summary, the algorithm identifies regions of the parameter space that are far from the initial set, draws a new LHS within those regions, and then merges it with the original samples. As a by-product, we introduce a new metric, the LHS degree, which quantifies the deviation of a given design from an LHS distribution. Our public implementation is distributed via the Python package expandLHS.