Spatial organization of different cell types in tissues have been shown to be important factors in many important biological processes such as aging, infection and cancer [\citenum{blise2022single}]. In particular, organization of the cells in a tumor microenvironment (TME) has been shown to play a crucial role in treatment response, disease pathology and patient outcome [\citenum{moffitt2022emerging}]. Spatial LDA [\citenum{chen2020modeling}] is a general purpose probabilistic model that has been used to discover novel microenvironments in settings such as Triple Negative Breast Cancer (TNBC) and Tuberculosis infections. However, the implementation of Spatial LDA proposed in [\citenum{chen2020modeling}] uses variational inference for learning model parameters and unfortunately does not scale well with dataset size and does not lend itself to speed-up via GPUs / TPUs. As researchers begin to collect larger in-situ multiplexed imaging datasets, there is a growing need for more scalable approaches for analysis of microenvironments. Here we propose a VAE-style network which we call \textit{Neural Spatial LDA} extending the auto-encoding Variational Bayes formulation of classical LDA from [\citenum{srivastava2017autoencoding}]. We show Neural Spatial LDA achives significant speed-up over Spatial LDA while at the same time recovering similar topic distributions thus enabling its use in large data domains.